IJREAS_dec2011
-
Upload
syed-abdul-samad -
Category
Documents
-
view
70 -
download
6
Transcript of IJREAS_dec2011
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 1 http://www.euroasiapub.org
HEALTH, SAFETY, ENVIRONMENT AND CLIMATE Dr. Bhuvnender Chaudhary*
Saurabh Tripathi**
Nidhi Singh***
ABSTRACT
Sustainable development in energy sector means progress in society, keeping in mind,
survival of all by containing the negative retrospective effect on life, health, sustainability it
all depend on our life style, habit, basic needs and desires.
Do we contain our needs and think about wastage of resources and management of resources
with efficient upgraded modern technology. The answer is if we look at the aspiration of
everybody to conquer s Mars and Moon and a mass wealth .do they think for living simple
healthy life of safety in clean environment, pure climate and atmosphere. On the other side,
are we fallows environmental laws, conventions and guide lines or all these remain in books,
papers and up to discussion or seminar only. If we are really sincere about sustainability,
health, safety, environment and climate then we have to go beyond it, means contain our
needs and desire, educate masses specially poor and down trodden, contract the expansion
and growth of population, but practically in real term we think for material growth,
prosperity, comforts, enjoyment and quench our greed’s of amassing wealth, rather living
simple life. Which is against the principle of sustainability, that is why we see and experience
earthquake, tsunami, flood, eruption of volcanoes because we keep on disturbing the balance
of earth, when earth balance its credit with debit then we think for sustainability but soon we
forget and keep on disbalancing again, earth repeat the same process again but how long .
This equation of balance and disbalance will continue.
Keywords: Health, Safety, security, Environment.
*Dean, Department of Management Studies, Phonics Group of Institutions, Roorkee, Uttarakhand
**Asst. Professor, Department of Management Studies, Phonics Group of Institutions, Roorkee,
Uttarakhand
***Asst. Professor, Department of Management Studies, Dev Bhoomi Institute of
Technology , Dehradun
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 2 http://www.euroasiapub.org
INTRODUCTION
Development and environment work against one another. The development includes
enhancement of knowledge, creation of physical assets and infrastructures through rapid
industrializations all have direct bearing effect on the surrounding environment and climates
which affects our lives. The one who take the benefits of the developments and one who
unable to take benefits both are affected through this. The climate is changing very fastly if
we study the past track of hundred years the average temperature is arisen by 2 degree
Celsius, the many area on earth are drought prone and affected by flood as well as tsunami.
All this is result of rapid industrialization and developments which is emitting tons of
Carbons and Green house gases. Per-capita emissions are a country's total emissions divided
by its population. Per-capita emissions in the industrialized countries are typically as much as
ten times the average in developing countries. This is one reason industrialized countries
accepted responsibility for leading climate change efforts in the Kyoto negotiations. In
Kyoto, the countries that took on quantified commitments for the first period (2008–12)
corresponded roughly to those with per-capita emissions in 1990 of two tonnes of carbon or
higher. In 2005, the top-20 emitters comprised 80% of total GHG emissions (PBL, 2010. See
also the notes in the following section on the top-ten emitters in 2005). Countries with a
Kyoto target made up 20% of total GHG emissions.
Another way of measuring GHG emissions is to measure the total emissions that have
accumulated in the atmosphere over time (IEA, 2007,) over a long time period; cumulative
emissions provide an indication of a country's total contribution to GHG concentrations in the
atmosphere. Over the 1900-2005 periods, the US was the world's largest cumulative emitter
of energy-related CO2 emissions, and accounted for 30% of total cumulative emissions (IEA,
2007,). The second largest emitter was the EU, at 23%; the third largest was China, at 8%;
fourth was Japan, at 4%; fifth was India, at 2%. The rest of the world accounted for 33% of
global, cumulative, energy-related CO2 emissions.
TOP-TEN EMITTERS What follows is a ranking of the world's top ten emitters of GHGs for 2005 (MNP, 2007).
The first figure is the country's or region's emissions as a percentage of the global total. The
second figure is the country's/region's per-capita emissions, in units of tons of GHG per-
capita:
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 3 http://www.euroasiapub.org
S.No Countries Name Region's emissions as
a percentage
(1st figure)
Region's per-capita
emissions(units in Tons)
(2nd Figure)
1 China 17% 5.8
2 United States 16% 24.1
3 European Union 11% 10.6
4 Indonesia 6% 12.9
5 India 5% 2.1
6 Russia 5% 14.9
7 Brazil 4% 10.0
8 Japan 3% 10.6
9 Canada 2% 2.1
10 Maxico 2% 6.4
• These values are for the GHG emissions from fossil fuel use and cement production.
Calculations are for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and
gases containing fluorine (the F-gases HFCs, PFCs and SF6).
• These estimates are subject to large uncertainties regarding CO2 emissions from
deforestation; and the per country emissions of other GHGs (e.g., methane). There are
also other large uncertainties which mean that small differences between countries are
not significant. CO2 emissions from the decay of remaining biomass after biomass
burning/deforestation are not included.
• Excluding underground fires.
• Including an estimate of 2000 million tonnes CO2 from peat fires and decomposition of
peat soils after draining. However, the uncertainty range is very large.
• Industrialised countries: official country data reported to UNFCCC
Apart from this many hazards chemicals are used which is effecting the life of common man
aggressively no doubt the member countries of Kyoto Protocol agreements are taking pains to
reduce the level of emission of carbon in fact the carbon emission has been legalised, Carbon
trading permissible, the ground realties of reducing Carbon is not materialised the level
carbon emission is increasing day by day due to the stress of development and rising
population of the world. Till date there are so many conventions and agreements have been
taken place throughout the world and these are in following orders:
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 4 http://www.euroasiapub.org
• Aarhus Convention on Access to Information, Public Participation in Decision-
making and Access to Justice in Environmental Matters, Aarhus, 1998
• Alpine Convention together with its nine protocols
• ASEAN Agreement on Transboundary Haze Pollution
• Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR),
Canberra, 1980.
o Agreed Measures for the Conservation of Antarctic Fauna and Flora
o Convention for the Conservation of Antarctic Seals
o Convention for the Conservation of Antarctic Marine Living Resources
o Protocol on Environmental Protection to the Antarctic Treaty
• Anti-Ballistic Missile Treaty (ABM Treaty) (ABMT)
• Asia-Pacific Partnership on Clean Development and Climate
• Barcelona Convention for the Protection and Development of the Marine
Environment and Coastal Region of the Mediterranean Sea
• Basel Convention on the Control of Transboundary Movements of Hazardous Wastes
and their Disposal, Basel, 1989.
• Biological Weapons Convention (Convention on the Prohibition of the Development,
Production and Stockpiling of Bacteriological [Biological] and Toxin Weapons and
on their Destruction) (BWC)
• Bonn Agreement (environment)
• Carpathian Convention Framework Convention on the Protection and Sustainable
Development of the Carpathians
• Cartagena Protocol on Bio safety
• Chemical Weapons Convention
• China Australia Migratory Bird Agreement
• CITES Convention on the International Trade in Endangered Species of Wild Flora
and Fauna
• Climate Change Agreement
• Comprehensive Test Ban Treaty (CTBT)
• Convention for the Conservation of Antarctic Seals
• Convention for Co-operation in the Protection and Development of the Marine and
Coastal Environment of the West and Central African Region, Abidjan, 1981.
• Convention for the Protection and Development of the Marine Environment and
Coastal Region of the Mediterranean Sea Barcelona Convention, Barcelona, 1976.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 5 http://www.euroasiapub.org
• Convention for the Protection and Development of the Marine Environment of the
Wider Caribbean Region, Cartagena de India’s, 1983.
• Convention for the Protection of the Marine Environment and Coastal Area of the
South-east Pacific, Lima, 1981.
• Convention for the Protection of the Marine Environment of the North-east Atlantic
OSPAR Convention, Paris, 1992.
• Convention for the Protection of the Natural Resources and Environment of the South
Pacific Region, Nouméa, 1986.
• Convention of the Protection, Management and Development of the Marine and
Coastal Environment of the Eastern African Region, Nairobi, 1985.
• Convention on Access to Information, Public Participation in Decision-making and
Access to Justice in Environmental Matters Aarhus Convention, Aarhus, 1998
• Convention on Assistance in the Case of a Nuclear Accident or Radiological
Emergency (Assistance Convention), Vienna, 1986.
• Convention on Biological Diversity (CBD), Nairobi, 1992.
• Convention on Certain Conventional Weapons
• Convention on Civil Liability for Damage Caused during Carriage of Dangerous
Goods by Road, Rail, and Inland Navigation Vessels (CRTD), Geneva, 1989.
• Convention on Cluster Munitions
• Convention on Early Notification of a Nuclear Accident (Notification Convention),
Vienna, 1986.
• Convention on Fishing and Conservation of Living Resources of the High Seas
• Convention on Long-Range Trans boundary Air Pollution
• Convention for the Protection of the Marine Environment of the North-east Atlantic
OSPAR Convention, Paris, 1992.
• Convention on Nuclear Safety, Vienna, 1994.
• Vienna Convention on Civil Liability for Nuclear Damage, Vienna, 1963.
• Convention on the Conservation of European Wildlife and Natural Habitats
• Convention on the Conservation of Migratory Species of Wild Animals, (CMS),
Bonn, 1979.
• Convention on the International Trade in Endangered Species of Wild Flora and
Fauna, (CITES), Washington DC, 1973.
• Convention on the Prevention of Marine Pollution by Dumping Wastes and Other
Matter
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 6 http://www.euroasiapub.org
• Convention on the Prohibition of Military or Any Other Hostile Use of Environmental
Modification Techniques
• Convention on the Protection and Use of Transboundary Watercourses and
International Lakes (ECE Watr Convention), Helsinki, 1992.
• Convention on the Transboundary Effects of Industrial Accidents, Helsinki, 1992.
• Convention on Wetlands of International Importance Especially As Waterfowl
Habitat
• Convention to Combat Desertification (CCD), Paris, 1994.
• Convention on the Protection of the Black Sea against Pollution, Bucharest, 1992.
• Convention on the Protection of the Marine Environment of the Baltic Sea Area 1992
Helsinki Convention, Helsinki, 1992.
• Conventions within the UNEP Regional Seas Programme
• Convention on the ban of the Import into Africa and the Control of Transboundary
Movements and Management of Hazardous Wastes within Africa, Bamako, 1991.
o EMEP Protocol
o Nitrogen Oxide Protocol
o Volatile Organic Compounds Protocol
o Sulphur Emissions Reduction Protocols 1985 and 1994
o Heavy Metals Protocol
o POP Air Pollution Protocol
o Multi-effect Protocol (Gothenburg protocol) [5]
• Directive on the legal protection of biotechnological inventions
• Energy Community (Energy Community South East Europe Treaty) (ECSEE)
• Espoo Convention on Environmental Impact Assessment in a Transboundary Context,
Espoo, 1991.
• European Agreement Concerning the International Carriage of Dangerous Goods by
Inland Waterways (AND), Geneva, 2000.
• European Agreement concerning the International Carriage of Dangerous Goods by
Road (ADR), Geneva, 1957.
• FAO International Code of Conduct on the distribution and use of Pesticides, Rome,
1985.
• FAO International Undertaking on Plant Genetic Resources, Rome, 1983.
• Framework Convention on Climate Change (UNFCCC), New York, 1992.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 7 http://www.euroasiapub.org
• Geneva Protocol (Protocol for the Prohibition of the Use in War of Asphyxiating,
Poisonous or other Gases, and of Bacteriological Methods of Warfare)
• International Convention for the Prevention of Pollution from Ships
• International Convention for the Conservation of Atlantic Tunas (ICCAT), Rio de
Janeiro, 1966.
• International Convention for the Regulation of Whaling (ICRW), Washington, 1946.
• International Treaty on Plant Genetic Resources for Food and Agriculture
• International Tropical Timber Agreement, 1983 (expired)
• International Tropical Timber Agreement, (ITTA), Geneva, 1994.
• Kuwait Regional Convention for Co-operation on the Protection of the Marine
Environment from Pollution, Kuwait, 1978.
• Regional Convention for the Conservation of the Red Sea and the Gulf of Aden
Environment, Jeddah, 1982.
• Kyoto Protocol - greenhouse gas emission reductions
• Migratory Bird Treaty Act of 1918
• Montreal Protocol on Substances That Deplete the Ozone Layer, Montreal, 1989.
• North American Agreement on Environmental Cooperation
• Protocol on Environmental Protection to the Antarctic Treaty
• Putrajaya Declaration of Regional Cooperation for the Sustainable Development of
the Seas of East Asia, Malaysia, 2003.
• Ramsar Convention Convention on Wetlands of International Importance, especially
as Waterfowl Habitat, Ramsar, 1971.
• Rotterdam Convention on the Prior Informed Consent Procedure for Certain
Hazardous Chemicals and Pesticides in International Trade, Rotterdam, 1998.
• Stockholm Convention Stockholm Convention on Persistent Organic Pollutants
Stockholm, 2001.
• Treaty Banning Nuclear Weapon Tests in the Atmosphere, in Outer Space, and Under
Water
• Comprehensive Test Ban Treaty 1996
• United Nations Convention on the Law of the Sea
• United Nations Convention to Combat Desertification
• United Nations Framework Convention on Climate Change
• Vienna Convention for the Protection of the Ozone Layer, Vienna, 1985, including
the Montreal Protocol on Substances that Deplete the Ozone Layer, Montreal 1987.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 8 http://www.euroasiapub.org
• Vienna Convention on Civil Liability for Nuclear Damage, Vienna, 1963.
• Waigani Convention to Ban the Importation into Forum Island Countries of
Hazardous and Radioactive Wastes and to Control the Transboundary Movement and
Management of Hazardous Wastes within the South Pacific Region, Waigani, 1995.
• Western Regional Climate Action Initiative
Fact is this that these convections and meet remains up to the ceremonial levels and violation
and emission of rules continue unstoppably the developed country agree to pay billion of $
and supply of technology to other country for climate related study and project as par the
commitment of UNFCCC (United nation Forum work Convections on climate change) and
industrialized country have to contain the emission of gases and carbon but these countries
are fail to comply up to the expected stranded then start negotiation of reduction in
convention after convention the most recent was Berlin G 77 meet there after in coupon
Hagen in IPCC meet (inter governmental penal on climate change ).
Now the rules are flexible and compliance is monitor with commitments and penalties for
non compliance is executed this is beginning but not satisfactory there are lot many things to
do and miles to go head.
EFFECT OF CARBON EMISSION Rising temperature and climate change as seen in many part of world in shape of global
warming due to green house effect extreme cold condition, cloud bursting, reduction of
forestry cover, expansion of desertification and tsunami.
ENVIRONMENT AND DEVELOPMENT Physical environment is the part of a big natural process system that consists of various
subsystems as atmosphere, hydrosphere, lithosphere and biosphere that are uniquely
interactive in nature. These various subsystems are closely inter-linked through their own
natural processes. According to Odum (1971) and Trunk et al. (1978) Dynamic equilibrium
can be achieved naturally, but the time frame of the whole process depends on the magnitude
of the disturbance. Dynamic equilibrium in nature cannot be achieved if the change is too big
to handle.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 9 http://www.euroasiapub.org
Figure1: Components within a physical environment
ENVIRONMENTAL DEGRADATION Development will always cause changes to the physical environment. Under natural condition
changes can be absorbed by the physical environment through interactions of the various
components to attain a dynamic equilibrium state. Actually the physical environment is
capable of absorbing impact as long as it does not exceed its optimum level. If the optimum is
exceeded the physical equilibrium will start to deteriorate. Interaction between each of
physical subsystems is important to human beings as they are part of physical environment.
Each and every component of physical environment is capable of fulfilling various human
needs.
Figure 2: Environmental degradation resulting from interaction between human use system
and natural process system
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 10 http://www.euroasiapub.org
EFFECTS OF ENVIRONMENTAL DEGRADATION One of the greatest challenges facing humanity is environmental degradation, including
deforestation, desertification, pollution, and climate change – an issue of increasing concern
for the international community. Environmental degradation increases the vulnerability of the
societies it affects and contributes to the scarcity of resources.
Climate change will lead to an increase in the intensity and frequency of weather extremes,
such as heat waves, floods, droughts and tropical cyclones. The people hardest hit by climate
change and environmental degradation are those living in the most vulnerable areas,
including coastal communities, small island nations, Sub-Saharan Africa and Asian delta
regions. It is the poorest of the poor, who lack the resources to prepare, adapt and rebuild,
that are most affected.
Environmental degradation can lead to a scarcity of resources, such as water and farmable.
Extreme weather events, such as severe flooding, increase the spread of waterborne diseases,
such as malaria and diarrhoea.
The effects of the major environmental problems on both health and
productivity are: a. Water pollution and water scarcity: As per the estimation of UN, more than two million
deaths and billions of illnesses a year are attributable to water pollution. Water scarcity
compounds these health problems. Productivity is affected by the costs of providing safe
water, by constraints on economic activity caused by water shortages, and by the adverse
effects of water pollution and shortages on other environmental resources such as, declining
fisheries and acquifer depletion leading to irreversible compaction.
b. Air pollution: As per the estimation of UN, urban air pollution is responsible for
300,000—700,000 deaths annually and creates chronic health problems for many more
people. Restrictions on vehicles and industrial activity during critical periods affect
productivity, as does the effect of acid rain on forests and water bodies.
c. Solid and hazardous wastes: Diseases are spread by uncollected garbage and blocked
drains; the health risks from hazardous wastes are typically more localized, but often acute.
Wastes affect productivity through the pollution of groundwater resources.
d. Soil degradation: Depleted soils increase the risks of malnutrition for farmers.
Productivity losses on tropical soils are estimated to be in the range of 0.5-1.5 per cent of
GNP, while secondary productivity losses are due to siltation of reservoirs, transportation
channels and other hydrologic investments.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 11 http://www.euroasiapub.org
e. Deforestation: Death and disease can result from the localized flooding caused by
deforestation. Loss of sustainable logging potential and of erosion prevention, watershed
stability and carbon sequestration provided by forests are among the productivity impacts of
deforestation.
f. Loss of biodiversity: The extinction of plant and animal species will potentially affect the
development of new drugs; it will reduce ecosystem adaptability and lead to the loss of
genetic resources.
g. Atmospheric changes: Ozone depletion is responsible for perhaps 300,000 additional
cases of skin cancer a year and 1.7 million cases of cataracts. Global warming may lead to
increase in the risk of climatic natural disasters. Productivity impacts may include sea-rise
damage to coastal investments, regional changes in agricultural productivity and disruption of
the marine food chain.
ENVIRONMENTAL MANAGEMENT Management of the environment involves the application of acquired knowledge about the
environment with the aims of reducing, conserving or preventing further degradation.
Management of the environment has to take into consideration detail measurements and
observations about the environment through space and time and the social institutions
involved in managing the environment.
An example of a multi-disciplinary framework in environmental management. In the figure,
environmental management is surrounded by problems from every component of the natural
process system as depicted by conservation of habitat and species diversity (biosphere), air
pollution (atmosphere), water pollution (hydrosphere), and land pollution (lithosphere). These
problems are part of the environment that requires management and can only be precisely
identified through environmental science, which is important in order to have an in-depth
knowledge of the physical environment components. However, environmental science alone
is inadequate in a management system since environmental management requires knowledge
on culture, socio-economy and its impsacts. Furthermore, there must also be environmental
ethics to control human actions, concrete relationship between the federal and state
authorities, the support of non-governmental organizations, the private sector and the general
public.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 12 http://www.euroasiapub.org
Figure 3: Environmental management framework
HOW STAINABLE DEVELOPMENT 1. Execution of environmental Laws and convention: Merely framing laws and
organizing meets and convention are not sufficient. One has to think beyond these
frame work.
2. Social development : Merely developing lofty tower and high structure is not
sufficient unless the lower strata of society is provided alternative means of lively
hood for sustenance
3. Education and awareness: the present level of education and mass awareness is not
sufficient to cope up with the required sustainable development all efforts goes into
vein.
4. Contain needs and desire: the environment pollution and hazardous affects in the
society is arising due to raising need and desires of common man and lust amass more
and more without taking it to consideration the side effects on the quality of life in
the society, not only on those who desire more but on those to who contain needs also
so the masses should be educated to cut their needs keeping in mind the good and bad
effects of use.
5. Containing populations: If we want to live in pollution free environment then we
have to control the growing population of those who are just burden and not
contributing towards the quality of life and polluting the environment without any
check as the population grow the consequential need of daily requirement will also
grow which has a direct impact on the bearing of environment.
FEDERAL –STATE RELATION
DEVELOPMET MASTER PLAN
MASS MEDIA
PRIVATE SECTOR
POLICY & LEGISLATION
R&D
EDUCATION & ETHICS
NGOS GENERAL PUBLIC
PLANNING
ENVIRONMENTAL MASTER PLAN
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 13 http://www.euroasiapub.org
6. Limit the greed for amassing wealth: If all the wealth is extracted in a day and
leaving nothing for future because the capacity of wealth in the earth is limited. The
every extraction from the earth will disbalance the equilibrium of earth which
generates environment disorder like drought, tsunami, flood in a continuous
phenomenon .that is why we must extract that much only which is essential for
sustenance.
7. Simple living and sober life style: The environment can be protected only if we live
in a simple and sober life style without artificial show-off in functions, ceremony’s,
festivals and marriages as all these occasions are full of different types of pollutions
which are very common now a days. As there is no proper check and regulation over
it as well as the maximum violation is created by the elite class who carelessly
involved in celebrations of special occasions more over.
8. Promotion of Green and environment friendly technology: No doubt we in India
have a law for pollution control and to check the use of polluting technology but even
then we are quit use to of using substandard, obsolete, outdated technology without
carrying its side effects on the quality of life over this earth.
9. Reward for the promoter of environment friendly efforts: We must reward to
those and keep on rewarding the good work carried for the promotion of Eco friendly
system as a effort to upgrade the quality of lives over the earth.
10. Funding of environment friendly projects: all those proposals which have positive
effect on the life of human beings and the quality of life on the earth needs to be
supported and financed as a gesture of goodwill and positivity for taking society
towards survival and growth under the sustainable development.
GO GREEN FOR LIFE OF HEALTH AND SUSTAINABILITY We’ve identified six major forces—what we call the six Cs—that are pushing clean tech into
the mainstream and driving the rapid growth, expansion, and economic necessity of clean
tech across the globe: climate, costs, capital, competition, China, and consumers.
Costs: Perhaps the most powerful force driving today’s clean-tech growth is simple
economics. As a medium to long term trend, clean-energy costs are falling as the costs of
fossil fuel energy, despite the drop in the price of oil in the second half of 2008, are going up.
The future of clean tech is going to be, in many ways, about scaling up manufacturing and
driving down costs. Recent advances in core technology and manufacturing processes have
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 14 http://www.euroasiapub.org
significantly improved performance, reliability, scalability, and cost of clean energy sources,
primarily solar and wind.
By contrast, in conventional fossil-fuel power such as coal and natural gas (which together
provide approximately 60% of the world’s electricity), the generating technologies are
mature, stable, and already widely deployed—so their technology costs are relatively steady
and predictable. What determines the price of conventional power is the cost of fuel—and the
price of fossil fuels, while certainly experiencing directional gyrations as we’ve seen in the
past year, has nearly always moved in the same general direction over the long term: up.
With solar, wind, small-scale hydroelectric, geothermal, and even the nascent technology of
ocean tide and wave generated electricity; the price-determining formula is just the opposite.
There is no cost of “fuel”—the sun, the breeze, the heat of the earth, the tides and waves
arrive free of charge daily.
Climate: Alarm is growing about the climate-change consequences caused by our continued
dependence on carbon-intensive, greenhouse gas (GHG)–emitting energy and transportation
sources, and manufacturing processes. The United Nations’ Intergovernmental Panel on
Climate Change warned in 2007 that global GHG emissions must be in decline by 2015 to
avert disastrous “runaway” climate change. And with insurance giants such as Swiss Re and
Munich Re thinking twice about climate impact on the issuance of their policies (try getting
an insurance policy for an oil rig in the Gulf of Mexico), the climate issue is coming front and
centre for companies, governments, and individuals.
This is driving clean-tech investment and deployment and becoming an increasingly
important factor in assessing investment risk factors. Global companies from DuPont to Wal-
Mart are investing heavily to promote energy efficiency and clean tech in their operations to
reduce their GHG contributions. “As an investor, do you believe that we’re going to take
climate change seriously in terms of legislation?” asks Mark Trexler, president of Trexler
Climate + Energy Services, a firm in Portland, Oregon, that advises companies and utilities
on carbon-reduction strategies. “To completely ignore it, in terms of investment decisions,
would be a terrible thing.”
Consumers: Rising energy prices, polluted ecosystems, and growing awareness of climate
change and the geopolitical costs associated with fossil fuels are driving a shift in consumer
attitudes and consumer demand for clean-tech products and services. That’s forcing
companies that sell to consumers – from appliance makers to auto manufacturers to Wal-Mart
– to produce and sell cleaner, more efficient products and to market them aggressively.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 15 http://www.euroasiapub.org
Who is driving this demand and growth, which is also evidenced by the steady expansion of
the LOHAS (Life of Health and Sustainability) demographic sector? Both early adopters,
who installed the first solar PV system in their neighbourhood or purchased an early-model
Toyota Prius, and mainstream customers, who are installing high-efficiency water heaters,
buying higher-mileage cars, insulating their homes with recycled denim, and demanding
efficient Energy Star appliances and windows.
These 21st century consumer preferences don’t seem to be slowed by the dramatic drop in
gasoline prices that began in the fall of 2008. A Consumer Federation of America survey in
February 2009 found that 76 percent of U.S. adults were still concerned about high gas prices
and an equal number worried about American dependence on oil from the Middle East.
Capital: An unprecedented influx of capital is changing the clean-tech landscape, with
billions of dollars, Euros, yen, and Yuan pouring in from a myriad of public and private
sector sources. Since the 1970s, investments in clean technology have moved from primarily
government research and development (R&D) projects to major multinationals, well-heeled
venture capitalists, and savvy individual investors.
General Electric, the world’s largest diversified manufacturer, plans to invest up to $1.5
billion a year in clean-tech R&D by 2010 as part of its “Ecomagination” business strategy.
Spain-based energy giants Iberdrola and Acciona are both poised to spend billions of dollars
building out their clean-energy portfolios, primarily wind power, over the coming years.
Toyota reportedly spends some $8 billion annually in R&D, much of it for hybrid and fuel-
cell development. Sanyo, the fourth largest solar cell manufacturer in the world behind Sharp,
Q-Cells, and Kyocera, has said it will invest $350 million over 5 years to expand its solar
operations as well.
The trend is significant. In 2008, despite its fourth-quarter downturn, venture capital
investments in clean tech (in North America, Europe, China, and India) grew 38% to $8.4
billion, according to research firm The Cleantech Group in San Francisco.
China; Clean tech is being driven by the inexorable demands being placed on the earth not
only by mature economies but also China, India, Brazil, Russia, and other rapidly developing
nations. Their expanding energy needs are driving major growth in clean-energy,
transportation, building, and water-delivery technologies.
China is emblematic of the resource-constraint issues facing our planet; China will not be
able to sustain its growth if it doesn’t widely embrace clean technology. The Chinese
government is starting to understand this and in 2006 committed to investing more than $200
billion over 15 years to meet nationally mandated targets for clean energy. China is planning
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 16 http://www.euroasiapub.org
to have 60 gigawatts of renewable energy (not including large hydroelectric) by 2010 and 120
GW by 2020.
Competition: This refers to competition among cities, regions, and nations to attract and
grow clean tech as a core industry for job creation and economic development. Thrust into
the national spotlight in the past year with the focus on “green jobs” as a major component of
U.S. economic recovery, clean tech as a development tool is gaining significant traction.
Whether promoting the retraining of laid-off steelworkers to build wind turbines or
employing inner-city job seekers to weatherize homes in their neighbourhoods, more
governments are seeking (and seeing) the benefits of clean tech-focused development efforts.
These powerful global forces—the six Cs—have put clean tech onto centre stage and
awakened a diverse range of stakeholders across the world. From Beijing to Berlin, from San
Francisco to Bangalore, the clean tech revolution is well under way. It will determine which
regions lead and prosper and which regions are left drowning in their own effluents, choking
on their own emissions, and struggling to compete in a world that is leaner, greener, and less
reliant on fossil fuels.
We believe the choice for investors, companies, governments, and individuals is simple,
especially as we seek a dramatic transition out of our current financial crisis. Be part of one
of the greatest business and economic shifts in recorded human history, or become extinct
like the dinosaurs whose fossils fuelled the last great industrial revolution.
CONCLUSION If we want to sustain ourselves then we have to protect environment and go for sustainable
development by containing our needs and desires putting check on growing populations by
educating the poor’s and down trodden for the protection of environment. The environment
protection depend on the life of health and sustainability that is why we have to go green
rather than offending the environment rules and laws. The government agencies need to be
strict and vigilant about the violation of environment rules. Youth can play the vital roles in
protection and promotion of environment consciousness among the different segment of
societies. The town planning and industrial developments must be environment friendly.
Bearing in mind the long term impact on the life of common man for the sustenance and
survival of biodiversity. The NGOs can play key role in educating the masses about the
negative impact of development and growth of polluting industries and rising population. The
environment education needs to be promoted beyond the curriculum of syllabi with practical
and meaningful approach for the survival of all. The management of resources within the
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 17 http://www.euroasiapub.org
means and scope of needs rather than false and illusive living style. Lastly we should be more
practical and close about the realities of life health, safety, environment and climate. If the
climate is neat and clean the environment will be pure and human friendly by which we will
safe and healthy. Health of today is the safety of tomorrow, precaution in today is the
protection for future. Which we can achieve through the sustainable development and active
and agile management of existing resources keeping in mind the needs of coming
generations. The capacity and strength of existing planet earth is limited if we manage over
needs in the lights of existing circumstantial environment then we will be certainly look
forward for the better future. In simple if our planet earth is not safe then we all be on the
dangerous note of destruction and If the nature and earth become violent then neither
offender nor the conservator will survive, there will be no question no answer, no officer no
sub-ordinate, no student no teacher, no king no slave, no raja no wazir, no agitation no
pollution, no poor no rich. All will equal and flat without any structure. As if the fire broke in
forest the fire will burn more live trees then dead wood.
REFERENCES 1. Banuri, T. et al. (1996). "Equity and Social Considerations.” in J.P. Bruce et al..
Climate Change 1995: Economic and Social Dimensions of Climate Change.
Contribution of Working Group III to the Second Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge,
U.K., and New York, N.Y., U.S.A. doi:10.2277/0521568544. ISBN 9780521568548.
2. Grubb, M. (July-September 2003). "The Economics of the Kyoto Protocol". World
Economics 4 (3): 143–189.
3. http://www.econ.cam.ac.uk/rstaff/grubb/publications/J36.pdf. Retrieved 2010-03-25.
4. PBL (24 February 2010). "Dossier Climate Change: FAQs. Question 10: Which are the
top-20 CO2 or GHG emitting countries?". Netherlands Environment Agency website.
5. http://www.pbl.nl/en/dossiers/Climatechange/FAQs/index.html?vraag=10&title=Which
%20are%20the%20top20%20CO2%20or%20GHG%20emitting%20countries%3F#10.
Retrieved 2010-05-01.
6. IEA (2007). "World Energy Outlook 2007 Edition- China and India Insights".
International Energy Agency (IEA), Head of Communication and Information Office, 9
rue de la Fédération, 75739 Paris Cedex 15, France. pp. 600.
http://www.iea.org/publications/free_new_Desc.asp?PUBS_ID=1927. Retrieved 2010-
05-04.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 18 http://www.euroasiapub.org
7. MNP (2007). "Greenhouse gas emissions of countries in 2005 and ranking of their per
capita emissions". Netherlands Environment Agency website.
http://www.pbl.nl/images/Top20-CO2andGHG-countries-in2006-2005(GB)_tcm61-
36276.xls. Retrieved 2010-05-01.
8. http://www.eurojournals.comejss_9_2_08.pdf (Environment Degradation and
Environmental Management By Jamaluddin Md. Jahi, Kadaruddin Aiyub, Kadir Arifin,
Azahan Awang )
9. http://www.buzzle.com/articles/how-do-humans-affect-the-environment.html
(Debopriya Bose)
10. http://www.saferenvironment.wordpress.com/2008/08/18/effects-of-environmental-
degradation/ (Partha Sharma)
11. http://www.suite101.com/content/10-ways-to-help-the-environment-that-are-healthy-
and-save-money-a261944 (Roger Vernon)
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 19 http://www.euroasiapub.org
MAXIMUM POWER POINT TRACKING USING PERTURBATION
AND OBSERVATION AS WELL AS INCREMENTAL
CONDUCTANCE ALGORITHM Manoj Kumar*
Dr. F. Ansari**
Dr. A. K. Jha***
ABSTRACT
This paper is comparative study of two type of maximum power point tracking (MPPT). The
optimisation of energy generation in a photovoltaic (PV) system is necessary to let the PV
cells operate at the maximum power point (MPP) corresponding to the maximum efficiency.
Since the MPP varies, based on the irradiation and cell temperature, appropriate algorithms
must be utilised to track the MPP. This is known as maximum power point tracking (MPPT).
Different MPPT algorithms, each with its own specific performance, have been proposed in
the literature. A so-called perturb and observe (P&O) as well as incremental conductance
method is considered here and both are compared. This two method is widely diffused
because of its low-cost and ease of implementation. When atmospheric conditions are
constant or change slowly, the P&O method oscillates close to MPP. However, when these
change rapidly, this method fails to track MPP and gives rise to a waste of part of the
available energy. A comparative study has been done on both the methods by using MATLAB
environment. The MPPT algorithm was set up and validated by means of MATLAB
simulations and experimental tests, confirming the effectiveness of the method.
Keywords: MPPT, MATLAB, Incremental Conductance, Perturb and Observe
*Gateway Inst. of Engg. & Tech
**BIT, Sindri
***Anupam Group of Industries
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 20 http://www.euroasiapub.org
INTRODUCTION
The comparisons between the PV water pumping system equipped with a Maximum power
point tracker (MPPT) and the direct coupled system without MPPT has been done with the
addition of a solar tracking using microcontroller. Microcontroller has been used to rotate the
panel so that we can utilize maximum renewable energy in more efficient way.
Also, the design and simulations of MPPT has been done using MATLAB to perform
comparative tests of the perturb and observe (P&O) and incremental Conductance (incCond)
algorithm. Simulations also verify the functionality of MPPT with a resistive load and then
with the DC pump motor load. The comparisons between the PV water pumping system
equipped with MPPT and the direct coupled system without MPPT has been done also solar
tracking using microcontroller has been used so that we can utilize maximum renewable
energy in more efficient way.
The two MPPT algorithms, P&O and incCond, discussed and are implemented in MATLAB
simulations and tested for their performance. Since the purpose is to make comparisons of
two algorithms, each simulation contains only the PV model and the algorithm in order to
isolate any influence from a converter or load. First, they are verified to locate the MPP
correctly under the constant irradiance, as shown in Figure 1.
Figure 1: Searching the MPP (1KW/m2, 25oC)The traces of PV operating point are shown in
green, and the MPP is the red asterisk.
MAXIMUM POWER POINT TRACKER When a PV module is directly coupled to a load, the PV module’s operating point will be at
the intersection of its I–V curve and the load line which is the I-V relationship of load. For
example in Figure 2, a resistive load has a straight line with a slope of 1/Rload as shown in
Figure 3. In other words, the impedance of load dictates the operating condition of the PV
module. In general, this operating point is seldom at the PV module’s MPP, thus it is not
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 21 http://www.euroasiapub.org
producing the maximum power. A study shows that a direct-coupled system utilizes a mere
31% of the PV capacity [1]. A PV array is usually oversized to compensate for a low power
yield during winter months. This mismatching between a PV module and a load requires
further over-sizing of the PV array and thus increases the overall system cost. To mitigate this
problem, a maximum power point tracker (MPPT) can be used to maintain the PV module’s
operating point at the MPP. MPPTs can extract more than 97% of the PV power when
properly optimized [2]. This chapter discusses the I-V characteristics of PV modules and
loads, matching between the two, and the use of DC-DC converters as a means of MPPT. It
also discusses the details of some MPPT algorithms and control methods, and limitations of
MPPT.
Figure 2: PV module is directly connected to a (variable) resistive load
Figure 3: I-V curves of BP SX 150S PVmodule and various resistive loads Simulated with
the MATLAB model (1KW/m2, 25oC
MAXIMUM POWER POINT TRACKING ALGORITHMS The location of the MPP in the I–V plane is not known beforehand and always changes
dynamically depending on irradiance and temperature. For example, Figure 4 shows a set of
PV I–V curves under increasing irradiance at the constant temperature (25oC), and Figure 5
shows the I–V curves at the same irradiance values but with a higher temperature (50oC).
There are observable voltage shifts where the MPP occurs. Therefore, the MPP needs to be
located by tracking algorithm, which is the heart of MPPT controller. There are a number of
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 22 http://www.euroasiapub.org
methods that have been proposed. One method measures an open-circuit voltage (Voc) of PV
module every 30 seconds by disconnecting it from rest of the circuit for a short moment.
Then, after re-connection, the module voltage is adjusted to 76% of measured Voc which
corresponds to the voltage at the MPP [3]. The implementation of this open-loop control
method is very simple and low-cost although the MPPT efficiencies are relatively low
(between 73~91%) [3]. Model calculations can also predict the location of MPP; however in
practice it does not work well because it does not take physical variations and aging of
module and other effects such as shading into account. Furthermore, a pyranometer that
measures irradiance is quite expensive. Search algorithm using a closed-loop control can
achieve higher efficiencies, thus it is the customary choice for MPPT. Among different
algorithms, the Perturb & Observe (P&O) and Incremental Conductance (incCond) methods
are studied.
Figure 4: I-V curves for varying irradiance and a trace of MPPs (25oC)
Figure 5: I-V curves for varying irradiance and a trace of MPPs (50oC)
PERTURB & OBSERVE ALGORITHM The perturb & observe (P&O) algorithm, also known as the “hill climbing” method, is very
popular and the most commonly used in practice because of its simplicity in algorithm and
the ease of implementation. The most basic form of the P&O algorithm operates as follows.
Figure 6 shows a PV module’s output power curve as a function of voltage (P-V curve), at the
constant irradiance and the constant module temperature, assuming the PV module is
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 23 http://www.euroasiapub.org
operating at a point which is away from the MPP. In this algorithm the operating voltage of
the PV module is perturbed by a small increment, and the resulting change of power, ∆P, is
observed. If the ∆P is positive, then it is supposed that it has moved the operating point
closer to the MPP.
Thus, further voltage perturbations in the same direction should move the operating point
toward the MPP. If the ∆P is negative, the operating point has moved away from the MPP,
and the direction of perturbation should be reversed to move back toward the MPP.
Figure 6: Plot of power vs. voltage for BP SX 150S PV module (1KW/m2, 25oC)
INCREMENTAL CONDUCTANCE ALGORITHM To solve the problem of the P&O algorithm under rapidly changing atmospheric conditions
the incremental conductance (incCond) algorithm was proposed [1]. The basic idea is that the
slope of P-V curve becomes zero at the MPP, as shown in Figure 6. It is also possible to find
a relative location of the operating point to the MPP by looking at the slopes. The slope is the
derivative of the PV module’s power with respect to its voltage and has the following
relationships with the MPP.
(1)
(2)
(3)
The above equations are written in terms of voltage and current as follows.
(4)
If the operating point is at the MPP, the equation (4) becomes:
(5)
(6)
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 24 http://www.euroasiapub.org
If the operating point is at the left side of the MPP, the equation (4) becomes:
(7)
(8)
If the operating point is at the right side of the MPP, the equation (4) becomes:
(9)
(10)
Note that the left side of the equations (6), (8), and (10) represents incremental conductance
of the PV module, and the right side of the equations represents its instantaneous
conductance.
PI CONTROLLING OF MPPT As shown in Figure 7, the MPPT takes measurement of PV voltage and current, and then
tracking algorithm (P&O, incCond, or variations of two). The PI loop operates with a much
faster rate and provides fast response and overall system stability [4] [5]. The PI controller
itself can be implemented with analog components, but it is often done with DSP-based
controller [4] because the DSP can handle other tasks such as MPP tracking thus reducing
parts count.
Figure 7: Block diagram of MPPT with the PI compensator
COMPARISONS OF P&O AND INCCOND ALGORITHM The two MPPT algorithms, P&O and incCond, discussed are implemented in MATLAB
simulations and tested for their performance. Since the purpose is to make comparisons of
two algorithms, each simulation contains only the PV model and the algorithm in order to
isolate any influence from a converter or load. First, they are verified to locate the MPP
correctly under the constant irradiance, as shown in Figure 8.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 25 http://www.euroasiapub.org
Figure 8: Searching the MPP (1KW/m2, 25oC)
The traces of PV operating point are shown in green, and the MPP is the red asterisk
Next, the algorithms are tested with actual irradiance data provided by [6]. Simulations use
two sets of data, shown in Figure 9, the first set of data is the measurements of a sunny day in
April in Barcelona, Spain, and the second set of data is for a cloudy day in the same month at
the same location. The data contain the irradiance measurements taken every two minutes for
12 hours. Irradiance values between two data points are estimated by the cubic interpolation
in MATLAB functions.
Figure 9: Irradiance data for a sunny and a cloudy day of April in Barcelona, Spain [6]
On a sunny day, the irradiance level changes gradually since there is no influence of cloud.
MPP tracking is supposed to be easy. As shown in Figure 10 & 11, both algorithms locate
and maintain the PV operating point very close to the MPPs (shown in red asterisks) without
much difference in their performance.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 26 http://www.euroasiapub.org
Figure 10: Traces of MPP tracking on a sunny day (25oC) by using P&O algorithm.
Figure 11: Traces of MPP tracking on a sunny day (25oC) by using inccond algorithm.
On a cloudy day, the irradiance level changes rapidly because of passing clouds. MPP
tracking is supposed to be challenging. Figure 12 shows the trace of PV operating points of
P&O algorithm and Figure 13 for incCond algorithm. For both algorithms, the deviations of
operating points from the MPPs are obvious when compared to the results of a sunny day.
Between two algorithms, the incCond algorithm is supposed to outperform the P&O
algorithm under rapidly changing atmospheric conditions [1]. A close inspection of Figure 12
& 13 reveals that the P&O algorithm has slightly larger deviations overall and some erratic
behaviours (such as the large deviation pointed by the red arrow). Some erratic traces are,
however, also observable in the plot of the incCond algorithm.
Figure 12: Traces of MPP tracking on a cloudy (25oC) by using P&O algorithm
Figure 13: Traces of MPP tracking on a cloudy day (25oC) by using inccond algorithm.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 27 http://www.euroasiapub.org
Table 1: Comparison of the P&O and incCond algorithms on a cloudy day.
P&O Algorithm incCond Algorithm
Total energy(simulation) 479.63Wh 479.69Wh
Total energy (theoretical
max)
480.38Wh 480.38Wh
Efficiency 99.85% 99.86%
Total electric energy produced with the incCond algorithm is narrowly larger than that of the
P&O algorithm. The MPP tracking efficiency measured by {Total Energy (simulation)} ÷
{Total Energy (theoretical max)} ×100% is still good in the cloudy condition for both
algorithms, and again it is narrowly higher with the incCond algorithm. The irradiance data
are only available at two-minute intervals, thus they do not record a much higher rate of
changes during these intervals. The data may not be providing a truly rapid changing
condition, and that could be a reason why the two results are so close. Also, further
optimization of algorithm and varying a testing method may provide different results. The
performance difference between the two algorithms, however, would not be large. There is a
study showing similar results [3]. The simulation results showed the efficiency of 99.3% for
the P&O algorithm and 99.4% for the incCond algorithm. The experimental results showed
96.5% and 97.0%, respectively, for a partly cloudy day.
MPPT SIMULATIONS WITH RESISTIVE LOAD First, MPPT with a resistive load is implemented in MATLAB simulation and verified. The
selection of the P&O algorithm permits the use of the output sensing direct control method
which eliminates the input voltage and current sensors. The MPPT design, therefore, chooses
the P&O algorithm and the output sensing direct control method because of the advantage
that allows of a simple and low cost system. The simulated system consists of the BP SX
150S PV model, the ideal Cúk converter, the MPPT control, and the resistive load (6 ). The
MATLAB function that models the PV module is the following:
(11)
The function, bp_sx150s, calculates the module current (Ia) for the given module voltage Va),
Irradiance (G in KW/m2), and module temperature (T in oC). The operating point of PV
module is located by its relationship to the load resistance (R) as explained in Section.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 28 http://www.euroasiapub.org
(12)
The irradiance (G) and the module temperature (T) for the function (11) are known variables,
thus it is possible to say that Ia is the function of Va hence Ia = f(Va). Substituting this into
the equation (12) gives:
(13)
Knowing the value of R enables to solve this equation for the operating voltage (Va).
MATLAB uses fzero function to do so. Appendix for details. Placing Va, back to the
equation (11) gives the operating current (Ia).
For the direct control method, each sampling of voltage and current is done at a periodic
steady state condition of the converter. The following equations describe the input/output
relationship of voltage and current, and they are used in the MATLAB simulation.
(14)
(15)
Where: D is the duty cycle of the Cúk converter.
The simulation is performed under the linearly increasing irradiance varying from 100W/m2
to 1000W/m2 with a moderate rate of 0.3W/m2 per sample. Figure 14 and 15 show that the
trace of operating point is staying close to the MPPs during the simulation. Figure 16 shows
the relationship between the output power of converter and its duty cycle. Figure 17 shows
the current and voltage relationship of converter output. Since the load is resistive, the current
and voltage increase linearly with the slope of 1/Rload on the I-V plane.
Figure 14: operating point between o/p power vs voltage
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 29 http://www.euroasiapub.org
Figure 15: operating point between module current vs module voltage
Figure 16: operating point between o/p power vs duty cycle.
Figure 17: operating point between o/p current vs o/p voltage
RESULT ANALYSIS The comparative study of P&O algorithm and incCond algorithm has been observed by
MATLAB simulation. For both algorithms, the deviations of operating points from the MPPs
are obvious when compared to the results of a sunny day. Between two algorithms, the
incCond algorithm is supposed to outperform the P&O algorithm under rapidly changing
atmospheric conditions.
The theoretical study of solar tracking system has been studied which can provide more
benefits compare to simple photovoltaic system. We can utilize maximum renewable energy
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 30 http://www.euroasiapub.org
source by solar tracking system, although we can get more solar energy by connecting more
number PV system either series or parallel but it will be more complex as well as costly.
A close inspection reveals that the P&O algorithm has slightly larger deviations overall and
some erratic behaviours (such as the large deviation) pointed by the red Some erratic
behaviour, however, also observable in the plot of the incCond algorithm. Total electric
energy produced with the incCond algorithm is narrowly larger than that of the P&O
algorithm. The MPP tracking efficiency measured by {Total Energy (simulation)} ÷ {Total
Energy (theoretical max)} ×100% is still good in the cloudy condition The simulation results
showed the efficiency of 99.3% for the P&O algorithm and 99.4% for the incCond algorithm.
The experimental results showed 96.5% and 97.0%, respectively, for a partly cloudy day.
REFERENCES 1. K.H.Hussein et al.,“Maximum Photovoltaic Power Tracking: an Algorithm for
Rapidly Changing Atmospheric Conditions” IEE Proceedings – Generation,
Transmission and Distribution – v. 142, page 59-64, January 1995.
2. D.P. Hohm, M. E. Ropp., “Comparative Study of Maximum Power Point Tracking
Algorithms” Progress in Photovoltaic: Research and Applications, page 47-62,
November 2002.
3. J.H.R.Enslin et al.,“Integrated Photovoltaic Maximum Power Point Tracking
Converter” IEEE Transactions on Industrial Electronics, Vol. 44,page 769-773,
December 1997.
4. Hua Chihchiang et al., “Implementation of a DSP controlled Photovoltaic System
with Peak Power Tracking” IEEE Transactions on Industrial Electronics, Vol. 45, No.
1, page 99-107, February 1998.
5. E.Koutroulis et al., “Development of a Microcontroller-Based, Photovoltaic
Maximum Power Point Tracking Control System” In proc. International Journal on
Power Electronics, Vol. 16, No. 1, page 46-54, January 2001.
6. Castañer, Luis & Santiago Silvestre Modelling Photovoltaic Systems, Using PSpice
John Wiley & Sons Ltd, 2002.
7. Abdelmalek Mokeddem et.al.,” Test and Analysis of a Photovoltaic DC-Motor
Pumping System”In proc. of ICTON on mediterranean winter conference. pp 1-
7,2007
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 31 http://www.euroasiapub.org
8. Taufik, Akihiro Oi et.al.,” Modeling and Simulation of Photovoltaic Water Pumping
System” IEEE Third Asia International Conference on Modelling & Simulation pp
497-502., May 2009.
9. Anna Mathew et al.,” MPPT Based Stand-Alone Water Pumping System”
International Conference on Computer, Communication & Electrical Technology –
ICCCET2011,pp 455-460., March 2011.
10. N. Hamrouni et.al.,”Measurements and Simulation of a PV Pumping Systems
Parameters Using MPPT and PWM Control Strategies” In proc. of IEEE
Mediterranean conference on Electro technical, pp 885-858., May 2006.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 32 http://www.euroasiapub.org
STUDY OF VARIOUS INDOOR PROPAGATION MODELS Er. Neha Sharma*
Dr. G.C.Lall*
ABSTRACT
Indoor Propagation modeling is demanded for the maintenance of indoors-wireless services.
Propagation models provide estimates of signal strength and time dispersion in many indoor
environments. These data are valuable in the design and installation of indoor radio systems. We propose improving existing channel models by building partitioning technique. Based on
the measurement results the easy-to-use empirical propagation predication models were
derived for both of the buildings with satisfactory accuracy. The result used to determine the
path loss exponent and standard deviation. It similarly shows that the RSS values Vs distance
help in determine the variation in multi-wall model and single wall.
Keywords: Wireless LAN, Ekahau Heat mapper, Visi-site survey, propagation modeling,
GPS.
*HCTM, Kaithal, Haryan
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 33 http://www.euroasiapub.org
1. INTRODUCTION Researchers have developed a variety of experimentally or theoretically based models to
predict radio propagation in various frequency bands and for various types of environments.
The past decades has witnessed a phenomenal growth in wireless communication. The need of
wireless technology in offices, and all the working places gives revolution to the indoor
propagation models. Indoor propagation is not influenced by weather conditions like rain,
snow or clouds as outdoor propagation, but it can be affected by the layout in the building
especially the use of different building material. Owing to reflection, refraction and diffraction
of radio waves by objects such as walls, windows, doors and furniture inside the building, the
transmitted signal often reaches the receiver through more than one path, resulting in a
phenomenon known as multi-path fading [1][2].
The mechanism behind electromagnetic waves propagation are diverse, but can generally be
attributed to reflection, scattering, refraction and diffraction. A signal radiated from an
antenna travels along one of the three routes: ground wave, sky wave, or line of sight
(LOS). Based on the operating frequency range, one of the three predominates. In [2], a
review of popular propagation models for the wireless communication channel is
undertaken. Macro cell (typically a large outdoor area), microcell (a small outdoor area), and
indoor environments are considered.
For a small network in a limited area, only manufacturer’s information on the coverage
range is sufficient to deploy the APs. The paper based on a site survey with a lot of
measurements and experimental decisions. One common approach employs surveying of
signal strength information in a particular area.
2. MODEL LOCATION This research began by measuring signal strengths. The result obtained the average signal
strength as well as the standard deviation at each location. Site survey using either a
standard wireless device with a testing software tool or special sophisticated equipment is
undoubtedly indispensable way to test existing WLAN networks - coverage, performance,
etc.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 34 http://www.euroasiapub.org
Fig. 2.1 Coverage area.
The experimental area shown with the help of Google Earth in Fig. 2.1. So, the main goal of
a site survey is to measure standard deviation
2.1 RECEIVED SIGNAL STRENGTH
Wi-Fi wireless networks are everywhere [3]. Visualize all Wi-Fi Networks: Ekahau
HeatMapper will display the coverage area of all the access points in the area on a map.
Fig.2.2 shows that the amplitude of signals varies for different AP’s, which is located at the
experimental area. This can help us to represent the strongest AP. VisiWave provides four
effective methods for capturing data (one point at a time, continuous walks through the
survey area, GPS positioning for outdoor surveys, and a custom dead-reckoning navigation
device) making data collection quick and easy [3][4]. Find Security Problems and Open
Networks:
HeatMapper displays if there are security issues in some networks, and shows the location of
unsecured networks. GPS help to take the distance in feet’s as well as in meters from the
transmitter to receiver.
Fig.2.2 Map Survey for nearby located WiFi’s.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 35 http://www.euroasiapub.org
There are lots of survey done with the help of GPS, which help in getting the exact distance
between the transmitter and the receiver. Ekahau Heat Mapper shows that the signals are
weak of AP1 as we gone far from the building. We can see that the RSS values from that AP
are getting weaker as we move away from it.
2.2 LIMITATION OF INDOOR PROPAGATION MODELS:
Improved Propagation models are required to achieve reliable and accurate propagation and
predictions. The various challenges facing the development of indoor propagation models are
as follows:
1) Propagation measurements primarily dependent on unavailable building construction
parameters such as wall thickness, materials, and indoor building structures.
2) A large number of prediction methods require computation of the effect of reflections
and transmissions and hence become time consuming and computationally ineffective
3) Most of the techniques are applicable to high frequencies thus the dimension of some
indoor structures may not necessarily satisfy the large dimensions compared to the
wavelength criterion required by these methods.
4) Small-scale fading- it causes great variation within a half wavelength. Multipath and
moving scatters cause it. Rayleigh, Ricean, usually approximates resulting fades or
similar fading statistics measurements also show good fit to Nakagami-m and Weibull
distributions [4].
3. PROPAGATION MODEL: 3.1 FREE SPACE PATH LOSS MODEL:
The spatial distribution of power at a distance d from a transmitter is, in general, a decreasing
function of d. A distance power law of the form represents this function
P=l/dm (3.1) For free space, m is equal to 2 and it is said that the power gain follows an inverse square
law. In an enclosed environment, however, this is not true anymore. I showed that when the
transmitter and receiver were placed in the same living room, in sight of each other, the
power decayed with a value of m ranging of 1.5 to 1.8. when the receiver was located within
a room off the hallway, m ranged from 3 to 4.
The path loss also varies with frequency. The measurement results indicate that loss through
floors is greater at the higher frequency. It is found that at wavelengths in the millimeter
range the radio wave cannot penetrate most common building materials such as brick and
concrete block and that signal attenuation occurs more rapidly with distance [5]. Therefore
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 36 http://www.euroasiapub.org
the millimeter waveband seems to be a good choice for providing broadband services in a
high-capacity frequency-reuse environment. The equation for FSPL is
(3.2)
Where:
▪ Is the signal wavelength (in meters),
▪ Is the signal frequency (in hertz),
▪ Is the distance from the transmitter (in meters),
▪ C is the speed of light in a vacuum, 2.99792458 × 108 meters per second.
Alexandra has given the values of m according to the building materials used in the
environment. The degree 01 signal attenuation depends on the type of materials the signal
encounters. Consequently, the construction materials can characterize the signal decay in an
indoor environment.
Fig. 3.1 Free Space Path Loss Model
We used visi-site survey software tool to verify the coverage of a specific AP and get a
rough idea of the RSS values related to that AP. After covering the distance of 10 meter
away from the source. It helps in creating the data for the survey which gives all the
information related to wifi signals.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 37 http://www.euroasiapub.org
Fig. 3.2 List if AP, SNR, MAC, SSID etc.
The Fig. 3.1 shows that in free space there is no loss of data between the transmitted signal
and receiver signal. The Fig. 3.2 shows AP list also contain MAC address, Max SNR, Min
SNR, Avg. SNR.
3.2. EMPIRICAL MODELS
Both theoretical and measurement based propagation models indicate that average received
signal power decreases logarithmically with distance. Empirical models help in reducing
computational complexity as well as increasing the accuracy of the predictions [6]. The
empirical model used in this study is Log-distance Path Loss Model.
3.2.1 Log-distance Path Loss Model In both indoor and outdoor environments the average large-scale path loss for an arbitrary
Transmitter-Receiver (T-R) separation is expressed as a function of distance by using a path
loss exponent, n [10][9]. The average path loss PL(d) for a transmitter and receiver with
separation d is:
PL(dB)= PL(d0) +10nlog(d), d0 (3.2)
where n is the path loss exponent which indicates the rate at which path loss increases with
distance d. Close in reference distance (d0) is determined from measurements close to the
transmitter.
3.2.2 LOG-NORMAL SHADOWING
Random shadowing effects occurring over a large number of measurement locations, which
have the same T-R separation, but different levels of clutter on the propagation path, is
referred to as Log-Normal Distribution [7]. This phenomenon is referred to as lognormal
shadowing. This leads to measured signals, which are vastly different than the average value
predicted by (3.2). To account for the variations described above equation (3.2) is modified
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 38 http://www.euroasiapub.org
as:
PL(dB) = PL(d0) +10nlog(d)+ Xσ (3.3)
where Xσ is a zero-mean Gaussian distributed random variable with standard deviation σ.
The close-in reference distance d0, the path loss exponent n, and the standard deviation σ,
statistically describe the path loss model for an arbitrary location having a specific T-R
separation.
Table 3.1 Path loss exponents for different environments.
3.2.3 TWO-RAY MODEL
Unlike statistical models, site specific propagation models do not rely on extensive
measurement, but a greater detail of the indoor environment is required to obtain an accurate
prediction of signal propagation inside a building. The received signal Pr for isotropic
antennas, obtained by summing the contribution from each ray, can be expressed as
(3.4)
where Pt is the transmitted power, r1 is the direct distance from the transmitter to the
receiver, r2 is the distance through reflection on the ground, and Γ(α) is the reflection
coefficient depending on the angle of incidence α and the polarization [8].
Fig.3.3 Two ray model.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 39 http://www.euroasiapub.org
The reflection coefficient is given by
(3.5)
where Θ = 90-α and a = 1/ε or 1 for vertical or horizontal polarization, respectively. εr is a
relative dielectric constant of the reflected surface []. The signal strengths from theoretical
and empirical models are compared in this study.
3.3 DETERMINISTIC MODELING APPROACH
Deterministic or semi-deterministic models are primarily based on electromagnetic wave
propagation theory being as close to physical principles as possible. Most of the models
known as ray tracing or ray launching are based on geometrical optics. Some simplifications
lead to viewing the radio wave propagation as optical rays. It can be seen that diffraction and
wave guiding effect of the corridor are considered. Since the multipath propagation can be
fully de- scribed, other space-time properties like time delays; angles of arrival etc. can be
determined. On the other hand, for a common planning only the propagation loss is sufficient
and the cost for the accuracy is enormous [4][7].
3.4. PARTITIONED MODEL
These models are very easy and fast to apply because the prediction is usually obtained from
simple closed ex- pressions. Also requirements on the input environment description are
“reasonable”. But, at the same time, only the propagation loss without great site-specific
accuracy can be predicted.
3.4.1 SINGLE-GRADIENT MULTI-FLOOR (SGMF) MODEL
The idea behind this model is that the distance dictates if the AP and receiver are located on
the same floor the path-loss from the AP to the receiver using a distance power-gradient. The
path-loss in the SGMF model is given by
Lp=L0+Lf(n)+10a *log(d) (3.6)
Where L0 is the path-loss over the first meter, Lf (n) is the attenuation attributed to each floor,
n is the number of floors between the transmitter and receiver, α is the distance- power
gradient, and d is the distance between the transmitter and receiver [8]. The Table 3.2 gives
the set of parameters suggested for three different environments.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 40 http://www.euroasiapub.org
Fig.3.4 The performance of the second floor and ground floor
Fig.3.5 Signal Strength for SGMF
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 41 http://www.euroasiapub.org
Table.3.2 Measurement for RSS values at first floor.
Fig. 3.4 displays that the performance of the signal strength with distance at second floor and
ground floor. This graph shows that the performance of second floor is better than ground
due to the presence of two AP’s at the same time. For showing the performance of first floor
we used MATLAB in Fig. 3.5.
The formula for the SGMF+BP model is given by:
(3.7)
Where Lp is the path-loss over distance d in dB, L0 is the path-loss over the first meter in dB,
Lf (n) is the attenuation attributed to each floor, n is the number of floors between the
transmitter and receiver, α1, and αE are the distance-power gradients for the respective path
sections, and dwbp is the dynamic AP specific wall breakpoint in meters [9].
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 42 http://www.euroasiapub.org
Fig.3.6 Performance of Partitioned Models at first floor
3.4.2 MULTI-GRADIENT SINGLE-FLOOR(MGSF) MODEL
The Multi-Gradient Single-Floor (MGSF) model most recently has been used to model the
WiFi propagation path-loss in indoor environments.
The distance partitioned MGSF model,
(3.9)
Where Lp is the path-loss over distance d in dB, L0 is the path-loss over the first meter in dB,
α1 and α2 are the distance-power gradients for the path sections one and two respectively,
and dbp is the breakpoint distance in meters. Table 4.1 gives suggested parameter sets for
three environments defined for 802.11 standard in reference [9][10].
Fig.3.7 Building Partitioned model
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 43 http://www.euroasiapub.org
Table 3.3 Indoor Residential LOS and NLOS values
The MGSF+BP model’s distance-power gradient was larger than the internal path distance-
power gradient, which does not fit with the known path-loss environment. The interior paths
should have higher path-loss due to interior wall and other physical obstructions. Fig.3.7
displays the building portioned model with the help of Table3.3 and Table 3.4.
Table 3.4 MGSF standards for calculations
The First floor of the building taken into consideration for the MGSF model. In this we
selected some distance from AP to calculate the path loss model in this area. Three AP’s is
assigned nearby so that the signal for each AP will be approximately same. In Fig.3.8 the
performance of partitioned models will be differ in single floor with multiple floors. The
distance-power gradient for this model is most likely artificially high due to the absence of
the exterior wall path-loss and should result in lower performance than the other model with
the exterior wall path-loss.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 44 http://www.euroasiapub.org
Fig.3.8 Coverage distance model prediction to empirical data comparison for each AP
The over predicted coverage could also be attributing to the higher mean RSS predicted by
the models. Fig.3.9 and Fig.3.10 Signal Strength for MGSF and MGSF-BP. To overcome this
short fall the footprints of the surrounding building could be added to future models.
Fig.3.9 Signal Strength for MGSF
5. RESULT ANALYSIS In this paper, we have pointed out the importance of propagation models in the development
of indoor wireless communications. Propagation models provide estimates of signal strength
and time dispersion in many indoor environments. These data are valuable in the design and
installation of indoor radio systems. Site-specific propagation modeling by solving
Maxwell’s equations is costly and impractical. The inclusion of diffraction theory can
broaden its application to lower radio frequencies. The accuracy of ray-tracing techniques
depends heavily on the accuracy and detail of the site-specific representation of the
propagation medium. The SGMF model had a higher peak performance but the MGSF
model had a slightly higher mean performance. The two methods for the design of large
wireless local area networks site survey and software planning were compared. The
drawbacks of site survey due to the time and space-varying environment were investigated
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 45 http://www.euroasiapub.org
using a simple experiment. The overview of available propagation models and its usage was
given.
REFERENCES 1. SUZUKI, H.: ‘A statistical model for urban radio propagation’, IEEE Trans.
Communication July 1977,COM-25, pp.673-680
2. HASHEMI, H.: ‘Simulation of the rural radio propagation channel’, IEEE Trans. Veh.
Technol., August 1979, VT-28, pp.213-224
3. BAJWA,AS.: ‘UHF wideband statistical model and simulation of mobile radio
multipath propagation effects’, IEE Proc. F., August 1985,132,(51,pp.327-333
4. RAPPAPORT, T.S., SEIDEL. S.Y., and SINGH, R : ‘900 MHz multipath propagation
measurements for US digital cellular radio telephone’, IEEE Trans. Veh. Technology
May 1990, VT-39, (2). pp.132-139
5. RAPPAPORT, T.S., SEIDEL, S.Y., and TAKAMIZAWA, IC: ‘Statistical channel
impulse response models for factory and open plan building radio communication
system design’, IEEE Trans. Communication May 1991.39, (5). pp.794-807
6. HASHEMI, H., THOLL. D., and MORRISON, G.: ‘Statistical modeling of the indoor
radio propagation channel part I’. Proc. IEEE Vehicular Technology Conference,
WC’92, Denver, CO, May 1992, pp.33&342
7. HASHEMI, H., LEE, D., and EHMAN. D.: ‘Statistical modeling of the indoor radio
propagation channel: part 11’. Proc. IEEE Vehicular Technology Conference, WC’92,
Denver, CO.,May 1992,pp.839843
8. HASHEMI, H.: ‘Impulse response modeling of indoor radio propagation channels’,
IEEE J. Sel. Areas Communication September 1993,SAC-11,pp.1788-1796
9. Ben Slimane, S. & Gidlund, “Performance of wireless LANs in radio channels”, IEEE
Multi-access, Mobility and Telegraphic for Wireless Communication December 2000,
5, 329-40.
10. MCKOWN, J.W., and HAMILTON, R.L.: ‘Ray tracing as a design tool for radio
networks’. IEEE Network, November 1991,5, (6),pp.27-30
11. www.metageek.net/products/inssider
12. www.earth.google.com/
13. www.visiwave.com/
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 46 http://www.euroasiapub.org
GIS SOLUTION FOR ENVIRONMENTAL MANAGEMENT AND
NATURAL RESOURCES DEVELOPMENT Nitin Sukhadevrao Goje*
Dr. Ujwal A. Lanjewar**
ABSTRACT Environmental management is inherently a spatial endeavor. Its data are particularly
complex as they require two descriptors; namely the precise location of what is being
described, as well as a clear description of its physical characteristics. For hundreds of
years, explorers produced manually drafted maps which served to link the “where is what”
descriptors. With an emphasis on accurate location of physical features, early maps helped
explorers and navigators chart unexplored territory.
The current surge of interest in environmental information springs from the convergence of
three profound world-wide trends: environmental awareness, liberation of public affairs, and
information technology. Degradation of environmental resources (air, water, soil and
biodiversity) has mobilized public opinion. This is because these resources intimately and
directly affect the quality of our lives. As a result the public demands to be better informed on
the state of the environment. In turn, governments and industries need spatial information in
order to manage and utilize the environmental resources in a sustainable manner.
The past two decades have witnessed dramatic advances in Information Technology. Spatial
data processing has advanced to the point where it matches the applications challenges
presented by the natural resource management. In addition, the Internet, Geomatics, and
Telecommunications are rapidly changing the way natural resources are being managed and
protected. These have provided more accurate and up-to-date information about resources;
further the information is readily available to would be users. In this paper we discuss the
contribution of Geographic Information System in Natural Resources Development and
Environment Management
Keywords: GIS, Environmental Management, Natural Resources Development, Data Model.
*Assistant Professor, ITM Institute of Management & Research, Kamptee, Nagpur.
**Professor, VMV Arts, JMT Commerce and JJP Science College, Nagpur.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 47 http://www.euroasiapub.org
INTRODUCTION Responsible and successful environmental management is necessary for protecting and
restoring the natural environment. The interdependency of the earth’s ecosystems and the
human impact on the environment present complex challenges to governments and
businesses as well as scientists and environmentalists in every discipline.
Geographic information system (GIS) technology is used to support and deliver information
to environmental managers and the public. GIS allows the combination and analysis of
multiple layers of location-based data including environmental measurements. The
environmental application areas of GIS are varied in terms of potential users, environmental
spheres, and the specific environmental issue being investigated. [7]
OBJECTIVE Objective of the paper is to study the various aspects of environmental management and
natural resources development and coming to the conclusion that the solutions provided by
the Geographic Information System.
GIS environmental management solutions enable organizations to
• Ensure accurate reporting with improved data collection.
• Improve decision making.
• Increase productivity with streamlined work processes.
• Provide better data analysis and presentation options.
• Model dynamic environmental phenomena.
• Create predictive scenarios for environmental impact studies.
• Automate regulatory compliance processes.
• Disseminate maps and share map data across the Internet.
LITERATURE SURVEY What is GIS?
GIS is a powerful software technology that allows a virtually unlimited amount of
information to be linked to a geographic location. Coupled with a digital map, GIS allows a
user to see locations, events, features, and environmental changes with unprecedented clarity,
showing layer upon layer of information such as environmental trends, soil stability, pesticide
use, migration corridors, hazardous waste generators, dust source points, Lake Remediation
efforts, and at-risk water wells. Effective environmental practice considers the whole
spectrum of the environment. GIS technology offers a wide variety of analytical tools to meet
the needs of many people, helping them make better decisions about the environment. [7]
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 48 http://www.euroasiapub.org
People in the environmental management community use GIS to organize existing
information and communicate that information throughout their organizations. GIS can be
used as a strategic tool to automate processes, transform environmental management
operations by garnering new knowledge, and support decisions that make a profound
difference on our environment.
GIS in Environmental Management and Natural Resources Development
GIS is a vital tool in natural resources development. The various aspects of resource
management it supports include storage and retrieval of data, interpretation and analysis of
the resource data, and development of the Resource Management Plans (RMP's). Resource
use alternatives are formulated, and the GIS is used to evaluate each in terms of
environmental impact, economic implications, acreage, and potential use conflict. One
important function of GIS is to assist in recognizing underlying patterns in data. These
patterns may be areas of forestland suitable for timber harvest or potential shifts in population
distribution. GIS simulations can be used to understand the direct and indirect effects of
human activities over long periods of time and over large areas.
By using the database integration capabilities of GIS, Planners and Resource Managers gain a
better understanding of the complex interrelationship between physical, biological, cultural,
economical, and demographic considerations around a specific resource. Access to this
information and its understanding makes it essential in making sound resource-use decisions.
This ensures balanced management and use of the resources. GIS is increasingly replacing
the traditional methods because it is faster, cost efficient and accurate. GIS analyses are hence
becoming routine in a significant number of field offices. [6]
Examples of GIS Application in Natural Resources Development
GIS applications are diverse and include water quality monitoring, modeling narcotic crop
sites, waste site assessment, analyzing effects of carbon dioxide etc. Some analyses relative to
forest are overlaying forested areas and logging areas to see what percentage of forest area is
in danger of degradation. Adding data on protected areas or biodiversity hot spots allows one
to see how these areas fit in the picture. Egregious problems, such as protected areas being
included in logging concessions can also be detected.[6]
One notable example is the detection of illegal oil and gas drainage from public lands by
wells on private lands. GIS reduces the process of drainage detection from several days done
manually to a few hours.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 49 http://www.euroasiapub.org
GIS ENVIRONMENTAL DATA MODEL Object of data modeling of environment are both its basic components: physical-geographical
sphere and social-economical one as well. Process of data modeling of environment can be
simply imagined on the basis of Following Fig 1.
Fig. 1. GIS Environmental data model [8]
Model of subjected environment created by geodetic, cartographic and photogrammetric
methods should have structure, contents and accuracy enabling to re-create it functional data
model. This data model should not only be able to be processed by computer technologies,
but also to be simply used for many purposes and users. [8]
FINDINGS & SUGGESTIONS Environmental developers and planners work together to bring the environmental
management community benefit and value from GIS. The model given in above figure gives
various benefits for environmental Management and Natural Resources Development.
Above model adds following benefits
• Database-sharing architecture that supports decision making and daily work tasks
• Interoperable system solutions for integrated workflow and data access
• Internet mapping solutions that support interagency collaboration projects
• Quality control processes that ensure accurate, high-quality data
• Worker-friendly designs that increase agency-wide access and application
• Scalability that supports and adapts to growing and evolving IT demand
We suggest that applying above data model in environmental management and natural
resources development can help in accessing accurate data with high speed. It required to
integrate above model by using various programming languages, databases and GIS
analytical engines.
CONCLUSION & FUTURE SCOPE Even though obstacles remain to their full deployment, Geomatics technologies now being
developed and demonstrated suggest natural resource applications that were not believed
possible using traditional techniques. As we progress towards the long talked about notion of
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 50 http://www.euroasiapub.org
integrated natural resources development and environmental management, some parallel
continuums along which the technology manifests are:
- The technology helps create integrated views of databases that span the levels of
map scale, detail and use. This helps in understanding the earth’s ecology.
- The technology meets the need for information presentation tools, as the pendulum
swings towards community place based management.
- The emergence of shared data infrastructure and accelerated information delivery,
e.g. Internet data ordering.
- Significant advances in data acquisition technology.
- Rapid improvement in data storage, retrieval and analysis.
REFERENCES 1. Fedra, K. (1993) GIS and environmental modelling. In: Environmental Modelling
with GIS (ed. by M. F. Goodchild, B. O. Parks & L. T. Steyaert), 35-50. Oxford
University Press.
2. Goodchild, M. F. (1993) Data models and data quality, problems and prospects. In:
EnvironmentalModelling with GIS (ed. byM. F. Goodchild, B. 0. Parks &L. T.
Steyaert), 94-103. Oxford University Press.
3. Harris, J., Gupta, S., Woodside, G. & Ziemba, N. (1993) Integrated use of a GIS and a
three-dimensional, finite-element model: San Gabriel Basin groundwater flow
analyses. In: EnvironmentalModelling with GIS (eu. by M. F. Goodchild, B. O. Parks
& L. T. Steyaert), 168-172. Oxford University Press.
4. Hassan H. M., Hutchinson C., 1992, Natural Resources and Environment Information
For Decision Making, The World Bank.
5. Johannsen C.V., Sanders J. L., 1982, Remote Sensing for Resource Management, Soil
Conservation Society Of America, Michigan, USA.
6. TS12.2 James Osundwa: The Role of Spatial Information in Natural Resource
Management International Conference on Spatial Information for Sustainable
Development Nairobi, Kenya 2–5 October 2001.
7. www.esri.com/environment/ GIS solution for Environmental Management.
8. ArtInAppleS, Ltd.: ArtGIS Training Program. ArtInAppleS, Ltd. Bratislava,
www.artinapples.sk.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 51 http://www.euroasiapub.org
PHYSICO-CHEMICAL ANALYSIS OF YAMUNA WATER AT
MATHURA Suman Yadav*
Dr. K.C. Gupta*
ABSTRACT
Physico-chemical Properties of water of Yamuna River at Mathura, (UP) were
studied. The time period of study was July 2009 to June 2010. Three sampling
stations were selected for study. The parameters studied were Temperature,
Turbidity, pH, DO, BOD, COD, Total Dissolved Solids and Suspended Solids.
Almost all the parameters were found above the tolerance limit.
Keywords: Pollution, Pollutants, D.O., B.O.D., C.O.D., Turbidity, Effluents, TDS,
TSS.
*Deptt. of Chemistry, Singhania University, Jhunjhunu, (Raj.)
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 52 http://www.euroasiapub.org
INTRODUCTION
Pollution is one of the most challenging problems today. The unwanted substances
are being regularly added to our environment, making it unsafe to live. Population
growth, rapid economic development, industrialisation and unconscious human
activities are slowly transforming our planet into a rotten place. The balance of
nature has been so adversely affected that we are facing with, frequent floods in
some areas and severe draught in the others.
Mathura (U.P.) is considered to be a historical and holy place, being the birth-place
of Lord Krishna, millions of pilgrims from every corner visit Mathura every year
and use to take bath in the holy river Yamuna. Their stay in the city causes a severe
sewage and garbage disposal problem. The sewage along with the garbage is
disposed off either directly or indirectly into the river Yamuna through a number of
wide drains and results in heavy water pollution.
Furthermore, Mathura is a fast developing city. A number of small and large
industries are working here, which use very fast, harmful and non-biodegradable
chemicals like sulphuric acid, silica powder, hydrochloric acid, detergents
including alkyl benzene sulphonate and linear alkyl sulphonate and several dyes
containing cyanides, arsenic, cadmium, mercury and led compounds. Their
menacing effects have been manifested in the form of the death of thousands of
aquatic organisms.
MATERIALS AND METHODS The sampling was done in second week of each month in glass bottles with
capacity 300 ml. The physico-chemical parameters of the water were determined on
the spots, with the help of ‘Portable water detection kit’ (Model no. CK-710,
manufactured by ‘Century Instruments Pvt. Ltd., Chandigarh). The temperature
was measured on the spot by using temperature sensitive electrodes of the portable
water detection kit. Other physico-chemical parameters from samples were
determined in the laboratory using the method suggested by APHA (1985) and
NEERI manual (1986). The results were compared with standard permitting
parameters as suggested by WHO and ISI. For digestional and pre concentration of
water samples, standard methods were followed (Chakraborty et.al. 1987 and
Subramaniam 1987).
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 53 http://www.euroasiapub.org
RESULTS AND DISCUSSION Temperature –
Temperature is an important physical factor which control the natural processes of
the environment. It was observed in accordance with the seasonal changes. It
ranged between 16.5–35.9 oC. It was higher in May, June and July and lower
during winter months i.e. December and January.
Turbidity –
Turbidity is generally caused by untreated and undecomposed organic matter,
sewage and industrial waste. It was very high in July and August because of the
‘Janmashtami’ and Shravan Maas’ when there is a mass gathering in the city and
millions of peoples take bath in Yamuna river. It was noted minimum 64 NTU and
maximum 131 NTU.
pH –
pH shows the acidic or alkaline nature of water. The water of river Yamuna was
found slightly alkaline. It ranged between 7.1 – 8.6. It showed similar trend with
Mathur et.al. (1987), Dakshini et.al (1979), Kumar and Sharma (2005) and Singh
et. al (1988).
TABLE -1 PHYSICO-CHEMICAL PARAMETERS OF RIVER YAMUNA FROM JULY 2009 TO JUNE 2010
(Average value of three sites)
Parameters Units Rains Winters Summers
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Temperature oC 30.8 30.4 23.4 22.7 22.4 19.1 16.5 21.1 23.9 28.0 33.4 35.9
Turbidity NTU 121 125 99 111 83 104 64 87 81 79 89 131
pH … 7.1 7.2 7.6 8.4 8.5 7.9 8.6 7.7 7.6 8.5 8.4 7.5
D.O. Mg/lit. 2.2 2.4 4.7 6.6 3.9 9.8 8.7 4.6 11.8 6.5 2.8 1.8
B.O.D. Mg/lit. 35.1 34.2 7.8 8.9 17.6 5.8 5.4 19.8 12.0 22.1 44.8 47.0
C.O.D. Mg/lit. 44.1 22.5 15.3 19.4 32.1 17.9 12.3 33.5 17.2 59.4 44.4 60.5
T.D.S. Mg/lit. 501 421 412 506 622 455 413 512 648 605 606 698
T.S.S. Mg/lit. 419 387 412 401 446 521 346 421 458 401 502 512
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 54 http://www.euroasiapub.org
Dissolved Oxygen :-
Dissolved oxygen is essential for the decomposition of chemical waste and dead
organic matter. It show variable trend. It was maximum in winter but lower in
summer. It ranged between 1.8 -11.8 mg/l. (Kumar & Sharma, 2004).
BOD :-
BOD is the amount of oxygen required by living aquatic organisms for their
physiological process. It was found very high in summer and comparatively low in
winter. It ranged between 5.4-47.0 mg/l. The findings were similar to those
observed by Kumar & Sharma (2005).
COD :-
It is the amount of oxygen required for the decomposition of chemical waste. A
high value of COD shows a higher accumulation of organic waste in the pond. It
was found higher during summer (60.5 mg/l) and lower during winter (12.3 mg/l).
Which was in accordance with the observations made by Shankar et. al (1986),
Reddy et. al (1985) and Sangu et. al. (1983).
TDS :-
Total dissolved solids also serve as indicator of pollution. Trend was found to be
highly fluctuating. It ranged between 412 - 698 mg/l. (Saxena et. al. ,1993 and
Siddiqui et. al, 1994).
TSS :-
Total suspended solids were found very fluctuating. TSS were higher in summer
and lower in winter and ranged between 346 - 521 mg/l. The findings were similar
to those observed by Mathur et. al (1987), Saxena et. al (1991) and Shahji et. al
(1993).
Summary & Conclusion –
From the above observations it was concluded that Yamuna river is highly polluted
and the use of its polluted water may cause various diseases. Remedial measures
are required to sustain the good quality of water and also to save the life of people.
REFERENCES
1. APHA (1992), “AWWA. WFCW in Standard Method for the examination of
water and waste water. American Public Health Association, New York.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 55 http://www.euroasiapub.org
2. Bhaumik B.M. and Singh A.K. (1985), ‘Phytoplankton population in relation
to physico-chemical factor of river Ganga at Patna’. Ind. J. Eco. 12(2) : 360-
364.
3. Dakshini K.M.M. and Soni J.K. (1979), ‘Water quality of sewage drains
entering Yamuna at Dehli’. Ind. J. Env. Hlth., Vol. 21, No. 4, 354-360.
4. Kumar Praveen and Sharma H.B. (2004) ‘ Studies on fluctuating trends in
some aquatic micro-organisms of Radha Kund at Mathura’ Flora & Fauna,
Vol. 10, no. 1, 22-24.
5. Kumar Praveen and Sharma H.B. (2005) ‘Physico-chemical characteristics
of lentic water of Radha Kunda (District Mathura)’. Ind. J. of Env. Sc. 9(1),
21–22.
6. Mathur A.,Y.C. Sharma, D.C. Rupainwar, R.C. Murthy and S.V. Chandra
(1987), ‘A study of river Ganga at Varanasi with special emphasis on heavy
metal pollution’. Poll. Res., 6(1):37-44.
7. Reddy M. and P.V. Venkateshwaralu (1985), ‘Ecological studies in the
paper mill effluents and their impact on river Tungabhadra : Heavy metals
and algal’. Proc. Ind. Acad. Sc. (Plant Sci.), 1985(3) :139-146.
8. Sangu R.P.S., P.D. Pathak and K.D. Sharma (1983), ‘Monitoring of Yamuna
river at Agra’. Proc. of the Nat. Confr. On river Poll. And human health.
9. Saxena K.K. and R.R.S. Chauhan (1993), ‘Physico-chemical aspects of
pollution in river Yamuna at Agra’. Poll. Res., 12(2) :101-104.
10. Shaji C. and R.J. Patel (1991), ‘Chemical and biological evaluation of
pollution in the river Sabarmati at Ahemadabad, Gujrat Phycos’. 30 : 981-
1000.
11. Shankar V., R.P.S. Sangu and G.C. Joshi (1986), ‘Impact of distillery
effluents on the water quality an eco-system of river Reh in Doon Valley’.
Poll. Res., 5(3&4): 137-142.
12. Sharma K.D., Lal N. and Pathak P.D. (1981), ‘Water quality of sewage
drains entering Yamuna at Agra’. Ind. J. Env. Hlth., Vol. 23 no. 2 : 118-122.
13. Shekhar S. (1985), ‘Studies on river pollution on river Cauveri’. Ind. J. Env.
St., 23 : 115-124.
14. Siddiqi Z.M., R.S. Panesar and S. Rani (1994), ‘Bio-chemical effect on few
sewerage disposal on the water quality of Sutlez river’. I.J.E.P., 14(10) :
740-743.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 56 http://www.euroasiapub.org
15. Singh J.P., P.K. Yadav and L.Singh (1988), ‘Pollution status on Sangam and
its adjoining river before the Kumbh Mela at Allahabad’. I.J.E.P., 8(11):
839-842.
16. WHO (1984), ‘International Standard for water.’ Third ed. Geneva.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 57 http://www.euroasiapub.org
DIVERSITY, ECOLOGICAL STRUCTURE AND CONSERVATION OF
HERPETOFAUNA IN TRANS YAMUNATIC REGION OF MATHURA Dr. H. B. Sharma*
Mamta Warman**
INTRODUCTION Biological diversity is fundamental to the fulfilment of human needs. An environment rich in
biological diversity offers the broadest array of options for sustainable economic activity, for
sustaining human welfare and for adapting to change. Loss of biodiversity has serious
economic and social costs for any country. The importance of biodiversity can be understood,
it is not easy to define the value of biodiversity, and very often difficult to estimate it. River
Yamuna , with a total length of around 1,370 kilometers (851 mi), is the largest tributary of
the Ganges in northern India. Yamuna is considered the most sacred among all the rivers as
per Hindu mythology. Its source is at Yamunotri, in the Uttarakhand Himalaya, in the
Himalayan Mountains. It flows through the states of Delhi, Haryana and Uttar Pradesh,
before merging with the Ganges at Allahabad. The cities of Delhi, Mathura and Agra lie on
its banks.With gradual increase in human population, pressure on land for agriculture,
urbanization, industrialization and developmental activities, the wetlands are severely
endangered and decaying day by day. Wetlands provide the habitats for fauna and flora.
Wetlands also serve as life support system by helping in water quality improvement, flood
control, recharging of ground water, storm protections, shoreline stabilization, and regulation
of hydrological regime, conservation of biological diversity and reduction of sediment loads
to the water bodies.
*HOD Dept.of ZOOLOGY, BSA College Mathura
**Scholar
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 58 http://www.euroasiapub.org
STUDY AREA Ecology of this area is a less explored subject with very rare studies on herpetofauna . We
describe as per our knowledge this is the first description of initial studies of this region. It
extends between 27°30′N 77°58 ′E longitude and 27.58°N 77.70°E latitudes, on the alluvial
flood plain of the ganga which is fed by its tributary Yamuna. It is touched by twelve
Village Development Committee at Mathura. Gokul barrage has been constructed to trap the
Yamuna. Reptiles and amphibian species were served during July, August & September
(Monsoon period). The impact of this precipitation is mostly influencing the water flow in the
rivers through flooding. During the non-monsoon period (October to June) the river flow
reduced significantly and some rivers stretches become dry. Just opposite of this, during
monsoon period the rivers receives significant amount of water, which is beyond their
capacity and resulting in flood. The River Yamuna also experiences such periods of drought
and floods. Yamuna River carries almost 80% of total annual flow during monsoon period.
The water flow reduces significantly during non-monsoon period and that too diverted from
river and extensively used for irrigation and drinking purpose, leaving very little or no water
flow in the river.
Satellite view of river Yamuna in Mathura
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 59 http://www.euroasiapub.org
Flow of river Yamuna through Mathura
METHODLOGY Each zone of the area is studied on the basis of following geographical divisions-
Forest
Water bodies- ponds, river, and wells.
Open fields
Rocky areas
All the water bodies were sampled for aquatic amphibians and soil was dug to determine the
presence of burrowing species. Each was randomly (biased) explored on the basis of habitat
structure and possibility and availability of the species. All important major and minor water
bodies, including seasonal rivulets were extensively explored for Herpetofaunal species.
Identification was done according to diagnostic keys provided by Smith (1935), Daniel
(1963). Sampling was conducted at each study site for these consecutive days . Mathura
Upstream at Vrindavan near Chirharan Ghat this location of river is being monitored to assess
the water quality of Yamuna before it enters Vrindavan – Mathura. Mathura Downstream at
Gokul Barrage. The site depicts the impact of wastewater discharges from
Mathura-Vrindavan city. Amphibian and reptile visual encounter surveys (ARVES) were a
standard method for terrestrial herpetofauna inventories (Campbell and Christman 1982,
Corn and Bury 1990, Crump and Scott 1994).In this method, the study sites were walked for
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 60 http://www.euroasiapub.org
the prescribed time period, systematically searching for the herpetofaunal species. The
species were searched throughout the region in water sources, nearby the water reservoirs and
also on the trees, to ensure the covering of each type of habitat. Many techniques have been
described for the inventory and monitoring of amphibian and reptile populations (Gibbons
and Semlitsch 1981; Heyer et al. 1994; Olson et al. 1997).Calling is the basic characteristic
of the male frogs. During the breeding season they produce sound to attract the female frogs.
The calling quality of each species is quite different and may use as a distinguishing character
of the frog species. This unique character of the frog was used in this method. Sound was
followed by the researcher to search the frogs. Identification based on their sound in
Rajasthan was made by Sharma (2005a,b).The sound spectrum identification and taxonomic
categorization is not only precise but environment friendly also because this does not involve
unnecessary killing and fixation of animal and data transformation is also very fast. Sharma
(2005a) and his associates are using this technique to monitor the anuran species in their
habitats Similarly some lizard species like H. brookii etc. Produce characteristic sound so
they were also we found during the survey by this method. Transect sampling was applied to
search reptiles in elevation gradients from lowlands to uplands depending on the area of study
sites. In this the map of study site was marked to over most of the vegetation and aquatic
habitats. Five plots, a 100 m ×6 m each, leaving a gap 50 m ×100 m was used as a transect.
Amphibians and Reptiles are often found in specific microhabitats or patches such as
underneath the logs of trees, holes and boulders. Patch sampling can be used to determine the
number, relative abundance and densities in such patches. Some reptile species are common
to breed in rock caves, loose barks etc. similarly amphibians are in breeding ponds and water
bodies. Data on the sex ratio, size and patterns could be collected at such sites.
OBSERVATION
In and around of trans yamuna are many notable wetlands like rivers, floodplain, riverine
marshes, fresh water marshes and ponds, seasonally flooded grassland, swamp forest,
reservoir, paddy fields etc. These wetlands are the suitable habitat for the herpetofauna, more
then 25 species were find out. Yamuna supports a wide variety of plants and animal species
.The river is home of many herpetofaunal species. The bank of river are one of the most
dwelling place of such species. All other visits were conducted during spring daytime hours
from march last to june mid of the couple of year. Species were identified visually (adults,
larvae, eggmasses) and the type of their wet microhabitat was described (brook, ditch, humic
substrate, inundated land, low-land stream, puddle, well, wet grass.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 61 http://www.euroasiapub.org
Source: Metrological Department Mathura
Observation Table
MIN MAX HUMIDITY RAINFALL TOTAL
RAINFALL
JAN 4.48 19.25 55.33 - -
FEB 6.37 22.87 51.33 - -
MAR 14.33 32.00 40.33 - -
APR 20.6 38.26 35.33 48 48
MAY 23.66 39.33 51.00 96 144
JUN 23.74 35.33 58.00 214 358
JUL 24.70 34.61 77.00 308 666
AUG 24.80 33.80 84.33 308 974
SEP 23.16 33.1 72.66 113 1087
OCT 20.19 33.29 65.00 - 1087
NOV 15.00 27.53 53.33 - 1087
DEC 10.25 22.38 58.33 - -
List of herpetofauna species found in trans Yamuna at Mathura
s.no Species Name Common Name
1 Hardella thurjii Brahminy Terrapin or Kali Kauntha
2 Starred tortoise Geochelone elegans
3 Kachuga tentoria Indian tent terrapin
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 62 http://www.euroasiapub.org
4 Kachuga tecta Roofed terrapin
5 Calotes versicolor Blood Sucker
6 Calotes jerdoni Jerdon’s Blood Sucker
7 Calotes rouxi Forest calotes
8 Sitana ponticeriana Fan throated lizard
9 Hemidactylus flavivirdis Northern house gecko
10 Hemidactylus brookii Brook’s Gecko
11 Cosymbotus platyurus Frilled house gecko
12 Mabuya carinata Common Skink
13 Lygosoma punctatus Snake skink
14 Varanus bengalensis Common Indian Monitor
15 Varanus flavescens Yellow Monitor
16 Varanus griseus Desert Monitor
17 Ramphotyphlops braminus Blind snake
18 Eryx conicus Russell’s earth boa
19 Eryx johni Boa boa
20 Python molurus bivittatus Indian Python or Rock Python
21 Elaphe radiata Copperhead or Trinket Snake
22 Ptyas mucosus Rat Snake or Dhaman snake
23 Argyrogena fasciolata Banded Racer
24 Spalerosophis diadema Royal or Diadem snake
25 Naja naja Indian Spectacled or Binocellate Cobra
26 Naja naja Indian Nag
27 Bungarus caeruleus Black Krait
28 Bufo stomaticus Marbled toad
29 Euphlyctis cyanophlyctis Skipping g Frog
30 Hoplobatrachus tigerinus Indian Bull Frog
31 Hoplobatrachus crassus Jerdon’s Bull Frog
32 Rana limnocharis Indian Cricket Frog
33 Sphaerotheca breviceps Indian Burrowing Frog
34 Microhyla inornata Burrowing microhylid frog
35 Uperodon systoma Marbled balloon frog
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 63 http://www.euroasiapub.org
RESULT Dash & Mahanta (1993) highlighted the need of extensive quantitative ecological studies on
the amphibian communities in the Indian ecosystems. Habitat destruction and alteration was
considered one of the most important factors (Blaustein & Wake, 1990; Khan, 1990; Ghate &
Pandhye, 1996; Ravichandran,1998; Alford & Richards, 1999). Daniels ( 1 9 9 5 , 1999a);
Molur & Walker (1998) highlighted the need of amphibian research and conservation in
India, in terms of amphibians taxonomy, range distribution, ecology and their conservation
requirements .Dash & Mahanta (1993) highlighted the need of extensive quantitative
ecological studies on the amphibian communities in the Indian ecosystems. . Ashley and
Robinson(1996) Observed that the road-kills of herpetofauna are a major cause of mortality
for a wide variety of taxa However, management decisions to implement actions for reducing
losses are based in economic realities, and herpetofauna often are low-profile species. Wilson
,et al. ,(2001)Recomanded that the biodiversity decline is one of the most serious
environmental problems, if not the most serious. Since it is a problem, it cries out for
solutions.
DISCUSSION
River Yamuna receives significantly high amount of organic matter, which is generally,
originates from domestic sources. For biodegradation, this organic waste requires oxygen,
causing significant depletion of dissolved oxygen in river water. The oxygen depletion not
only affects biotic community of the river but also affects its self-purification capacity. The
organic matter after biodegradation release nutrients in the water. High nutrients
concentration leads to Eutrophication, a condition characterized by significant diurnal
variation in dissolved oxygen concentration and excessive algal grown. Presently there is a
barrage in the Yamuna river at Mathura. The barrages have impact on characteristics of
Yamuna river which help forms some sort of reservoir towards upstream. This reservoir acts
as oxidation pond to treat the river water, which is helpful to survive faunal diversity.
Conserving biological diversity Ensuring sustainable use of the natural resource base
Minimizing pollution and wasteful consumption
The knowledge gaps in relation to status, distribution, impacts and institutions related to
biodiversity which need to be addressed for enhanced biodiversity conservation, community
empowerment, effective laws and policies and appropriate development models.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 64 http://www.euroasiapub.org
CONCULSION
The innumerable life forms harboured by the forests, deserts, mountains, other land, air and
oceans provide food, fodder, fuel, medicine, textiles etc. major problems confronting the
wetlands to decrease in biological diversity particularly endemic and endangered species.
Deterioration of water quality. Sedimentation & shrinkage in the areas. Large number of
people living in and around wetlands have been encroaching upon these areas and vast areas
have already been drained for agriculture, urban expansion and other purposes. Siltation is
one of the major problem. Deforestation and other anthropogenic activities have accelerated
soil erosion resulting in increased sedimentation rates and resultant shrinkage of wetlands.
Indiscriminate discharge of industrial/domestic effluent, leachates generated from improperly
disposed industrial solids waste, hazardous waste, municipal solid waste and biomedical
waste in the streams/land or the catchment area of the wetlands, not only deteriorate the water
quality of the system but the Toxic Pollutants i.e. trace heavy metals and trace organics are
absorbed in the biomass .The residual pesticides and fertilizer generated due to excessive use
of the commodities are carried away with rain water run off to the wetlands from the
catchment areas. Decrease in biological diversity particularly endemic and endangered
species. Clear cutting forests, draining wetlands and altering habitat may directly affect
amphibian population (Petranka et al., 1993; Semlitsch, 1998; Ernst & Rodel, ss2005). It
indicated that regional herpeto biodiversity status is in good position but needed
conservation. The study also showed the regular depletion of the herpetofauna. In whole
work it was realized that the common people of the region were not educated about the
necessity of the herprtofauna.
Conservation of wetland areas indiscriminately for aquaculture without proper land use
planning has also resulted in the destruction of a number of wetlands, these wetlands are the
primary requirement of conserve herpetonaul biodiversity. There are innumerable species, the
potential of which is not as yet known. It would therefore be prudent to not only conserve the
species we already have information about, but also species we have not yet identified and
described from economic point of view.
REFRENCES
1. Ashley, E.P., J.T.Robinson (1996). Road mortality of amphibians,reptiles and other
wildlife on the Long Point Causeway, Lake Erie, Ontario. Canadian Field-Naturalist
110, 403–412.
2. Blaustein, A.R. & D.B. Wake (1990).Declining amphibian populations: a
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 65 http://www.euroasiapub.org
3. Campbell, H.W., and S.P. Christman. (1982). Field techniques for herpetofaunal
community analysis. Pages 193-200 In N. J. Scott, Jr. (ed.), Herpetological
Communities, U.S.D.I. Fish and Wildlife Service, Wildlife Research Report 13,
Washington, D.C. 239 pp.
4. Corn, P. S., and R. B. Bury. (1990). Sampling Methods for Terrestrial Amphibians
and Reptiles. USDA Forest Service, General and Technical Report PNW-GTR-256,
34
5. Crump, M.L. and N.J. Scott, Jr. (1994). Visual encounter surveys. Pages 84- 92 in
W.R. Heyer, M.A. Donnelly, R.W. McDiarmid, L.C. Hayek, and M.S. Foster, eds.
Measuring and monitoring biological diversity: standard methods for amphibians.
Smithsonian Institution Press. Washington DC.
6. Crump, M.L. and N.J. Scott, Jr. (1994). Visual encounter surveys. Pages 84- 92 in
W.R. Heyer, M.A. Donnelly, R.W. McDiarmid, L.C. Hayek, and M.S. Foster, eds.
Measuring and monitoring biological diversity: standard methods for amphibians.
Smithsonian Institution Press. Washington DC.
7. Daniel, J.C. (1963a). Field guide to the amphibians of western India. Part I.
8. Daniel, J.C. (1963b). Field guide to the amphibians of western India. Part II.Journal of
the Bombay Natural History Society 60(3): 690-702.
9. Dash, M.C. & J.K. Mahanta (1993).Quantitative analysis of the community structure
of tropical amphibian assemblages and its significane to conservation. Journal of
Bioscience 18:
10. Gibbons, J. W. and R. D. Semlitsch. (1981). Terrestrial drift fences with pitfall traps:
an effective technique for quantitative sampling of animal populations. Brimleyana
No. 7:1-16. global phenomenon? TREE 5: 203-204.
11. Molur, S. & S. Walker (Editors) (1998). Report of the Conservation Assessment and
Management Plan (CAMP) Workshop for Amphibians of India (BCPP Endangered
Species Project). ZOO/ CBSG India, Coimbatore, 102pp.
12. Petranka, J. W.M.E. Eldridge & K.E.Haley (1993). Effects of timber harvesting on
southern Appalachian salamanders. Conservation Biology 7:363-370.
13. Sharma, K.K. (2005a). Sonotaxonomy: sound based taxonomy is a novel and
environment friendly approach in systematics. Journal of Cell Tiss. Research 5(3):
1-2.
14. Sharma, K.K. (2005b). Wildlife monitoring by sound analysis system – An authentic
and precise approach in wildlife management. National Conference on Environment
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 66 http://www.euroasiapub.org
and Natural Disaster Management, November 28-30, 2005. Dept. of Zoology,
Universityof Rajasthan, Jaipur. Abstract No. IL-10, pp 118.
15. Smith, M. A. (1935). The fauna of British India. Reptiles and Amphibians. Vol. II,
Sauria. Taylor and Francis, London, 305 pp.
16. Wilson L.D, McCranie J.R, and Espinal M.R. (2001)The eco-geography of the
Honduran herpetofauna and the design of biotic reserves. In: Johnson J. D, Webb, R.
G, Flores-Villela, O. A, editors. Mesoamerican herpetology: systematics,
zoogeography, and conservation. University of Texas at El Paso: Centennial
Museum;. pp. 109–158. Special Publication 1:1–200.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 67 http://www.euroasiapub.org
INDIAN STOCK MARKET TREND PREDICTION USING SUPPORT
VECTOR MACHINE M. Suresh Babu*
Dr. N. Geethanjali**
Prof. B. Satyanarayana***
ABSTRACT
Stock return predictability has been a subject of great controversy. The debate followed issues from market efficiency to the number of factors containing information on future stock returns. The analytical tool of support vector regression on the other hand, has gained great momentum in its ability to predict time series in various applications and also in finance (Smola and Schölkopf, 1998). Support vector machines (SVM) are employed to predict stock market dailytrends: ups and downs. The purpose is to examine the effect of macroeconomic information and technical analysis indicators on the accuracy of the classifiers. The construction of a prediction model requires factors that are believed to have some intrinsic explanatory power. These explanatory factors fall largely into two categories: fundamental and technical. Fundamental factors include for example macro economical indicators, which however, are usually only infrequently published. Technical factors are based solely on the properties of the underlying time series and can therefore be calculated at the same frequency as the time series. Since this study applies support vector regression to high frequent data, only technical factors are considered. It is found that macroeconomic information is suitable to predict stock market trends than the use of technical indicators. In addition, the combination of the two sets of predictive inputs does not improve the forecasting accuracy. Furthermore, the prediction accuracy improves when trading strategies are considered. Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of BSE30 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods. Keywords: Support Vector Machines, Classification, Stock Market, technical indicators. *Principal, Intel Institute of Science, Anantapur, Andhra Pradesh , India.
**Associate Professor, Department of Computer Science, S.K. University, Anantapur. India ***Professor & Chairman, Board of Studies, Department of Computer Science, Sri
Krishnadevaraya Univesity, Anantapur.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 68 http://www.euroasiapub.org
1. INTRODUCTION Forecasting stock market behavior is a very difficult task since its dynamics are complex and
non-linear. For instance, stock return series are generally noisy and may be influenced by
many factors; such as the economy, business conditions, and political events to name a few.
Indeed, empirical finance shows that publicly available data on financial and economic
variables may explain stock return fluctuations in the Indian Stock Market. For instance, a
number of applications have been proposed to forecast stock market returns with
macroeconomic variables with the use of neural networks and Bayesian networks and support
vector machines. On the other hand, technical indicators have been also used to predict stock
market movements using neural networks, adaptive fuzzy inference system, and fuzzy logic.
The literature shows that economic variables and technical indicators have achieved success
in predicting the stock market. However, none of the previous studies have compared the
performance of the economic information and technical indicators in terms of prediction
accuracy.
The financial market is a complex, evolutionary, and non-linear dynamical system. The field
of financial forecasting is characterized by data intensity, noise, non stationary, unstructured
nature, high degree of uncertainty, and hidden relationships. Many factors interact in finance
including political events, general economic conditions, and traders’ expectations. Therefore,
predicting finance market price movements is quite difficult. Increasingly, according to
academic investigations, movements in market prices are not random. Rather, they behave in
a highly non-linear, dynamic manner. The standard random walk assumption of futures prices
may merely be a veil of randomness that shrouds a noisy non-linear process.
Support vector machine (SVM) is a very specific type of learning algorithms characterized by
the capacity control of the decision function, the use of the kernel functions and the sparsity
of the solution. Established on the unique theory of the structural risk minimization principle
to estimate a function by minimizing an upper bound of the generalization error, SVM is
shown to be very resistant to the over fitting problem, eventually achieving a high
generalization performance.
Another key property of SVM is that training SVM is equivalent to solving a linearly
constrained quadratic programming problem so that the solution of SVM is always unique
and globally optimal, unlike neural networks training which requires nonlinear optimization
with the danger of getting stuck at local minima.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 69 http://www.euroasiapub.org
Some applications of SVM to financial forecasting problems have been reported recently. In
most cases, the degree of accuracy and the acceptability of certain forecasts are measured by
the estimates’ deviations from the observed values. For the practitioners in financial market,
forecasting methods based on minimizing forecast error may not be adequate to meet their
objectives. In other words, trading driven by a certain forecast with a small forecast error may
not be as profitable as trading guided by an accurate prediction of the direction of movement.
The goal of this study is to predict stock price movements only from the statistical properties
of the underlying financial time series and to explore the predictability of financial market
movement direction with SVM. Therefore, financial indicators are extracted from the time
series, which are then used by a support vector regression (SVR) to predict market
movement.
2.2.2 Support vector machines
Support Vector Machines (SVM) is a supervised statistical learning technique introduced by
Vapnik. It is one of the standard tools for machine learning successfully applied in many
different real-world problems. For instance, they have been successfully applied in financial
time series trend prediction. The SVM were originally formulated for binary classification.
The SVM seek to implement an optimal marginal classifier that minimizes the structural risk
in two steps. First, SVM transform the input to a higher dimensional space with a kernel
(mapping) function. Second, SVM linearly combine them with a weight vector to obtain the
output. As result, SVM provide very interesting advantages. They avoid local minima in the
optimization process. In addition, they offer scalability and generalization capabilities. For
instance, to solve a binary classification problem in which the output yϵ-1,+1 SVM seek
for a hyper-plane w.Φ xb 0 to separate the data from classes +1 and −1 with a maximal
margin. Here, x denotes the input feature vector, w is a weight vector, Φ is the mapping
function to a higher dimension, and b is the bias used for classification of samples. The
maximization of the margin is equivalent to minimizing the norm of w. Thus, to find w and b,
the following optimization problem is solved:
Minimize : || w ||2 + C Σni=1 ξi
S.t yi (w.Φ xib) ≥ 1 - ξi ξi ≥ 0 i = 1,......,n
where C is a strictly positive parameter that determines the tradeoff between the maximum
margin and the minimum classification error, n is the total number of samples, and
generalization and ξis the error magnitude of the classification.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 70 http://www.euroasiapub.org
The conditions ensure that no training example should be within the margins. The number of
training errors and examples within the margins is controlled by the minimization of the term:
The solution to the previous minimization problem gives the decision frontier:
f(x) = Σ yiαiΦ(xi)Φ(x) + b xi Where each αi is a lagrange coefficient. As mentioned before the role of the kernel function is
to implicitly map the input vector into a high-dimensional feature space to achieve better
separability. In this study the polynomial kernel is used since it is a global kernel. For
instance, global kernels allow data points that are far away from each other to have an
influence on the kernel values as well.
K(x,xi) = Φ(xi) Φ(x) = ((xi.x) + 1)d
where the kernel parameter d is the degree of the polynomial to be used. In this study, d is set
to 2. Finally, the optimal decision separating function can be obtained as follows:
2. THEORY Of SVM IN CLASSIFICATION The indicators are arbitrarily chosen among a high variety of financial indicators. The chosen
indicators include price differences, moving averages, relative strength and so called
stochastic indicators as shown in the figure. These indicators are then preprocessed in the
sense that the mean vector is subtracted and each indicator time series in divided by its
variance in order to receive indicator values with zero mean and unit variance. Before the
SVR model is trained, the parameters of the SVR model are optimized using a cross
validation procedure on a training set. After that, the optimized model is used to predict
financial market movement.
In the process of model selection, models are chosen only on the basis of performance over
out of sample data, in order to avoid the critique of judging the model on the basis of in
sample performance. The model selection is based on a cross validation procedure commonly
used in Data Mining.
Our main results show that stock market prediction based on support vector regression is
significantly outperforming a random stock market prediction. However, the prediction in
average is only correct in 50.69 percent of times with a standard deviation of 0.26 percent.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 71 http://www.euroasiapub.org
We present a basic theory of the support vector machine model. Let D be the smallest radius
of the sphere that contains the data (example vectors). The points on either side of the
separating hyperplane have distances to the hyperplane. The smallest distance is called the
margin of separation. The hyperplane is called optimal separating hyperplane (OSH), if the
margin is maximized. Let q be the margin of the optimal hyperplane. The points that are
distance q away from the OSH are called the support vectors.
Consider the problem of separating the set of training vector belonging to two separate
classes, G = {(xi, yi), i = 1, 2,.....,N} with a hyperplane wT ᵠ(x) + b = 0 (xi ϵ Rn is the ith
input vector, yi ϵ{−1, 1} is known binary target), the original SVM classifier satisfies the
following conditions:
wT ᵠ (xi) + b ≥1 if yi = 1, (1)
wT ᵠ (xi) + b ≤ if yi = −1, (2)
or equivalently,
yi[wT ᵠ (xi) + b] ≥ 1 i = 1, 2........ N, (3)
where ᵠ: Rn → Rm is the feature map mapping the input space to a usually high dimensional
feature space where the data points become linearly separable.
The distance of a point xi from the hyperplane is
(4)
The margin is 2/|w| according to its definition. Hence, we can find the hyperplane that
optimally separates the data by solving the optimization problem:
Min Ø(w) = ½ |w|2 (5)
under the constraints of Eq. (3).
The solution to the above optimization problem is given by the saddle point of the Lagrange
function
(6)
under the constraints of Eq. (3), where αi are the nonnegative Lagrange multipliers. So far
the discussion is restricted to the case where the training data is separable. To generalize the
problem to the non-separable case, slack variable ξi is introduced such that
(7)
Thus, for an error to occur the corresponding ξi must exceed unity, so is an upper
bound on the number of training errors. Hence, a natural way to assign an extra cost for errors
is to change the objective function from Eq. (5) to
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 72 http://www.euroasiapub.org
(8)
under the constraints of Eq. (7), where C is a positive constant parameter used to control the
tradeoff between the training error and the margin. In this paper, we choose C =50 based on
our experiment experiences. Similarly, solve the optimal problem by minimizing its Lagrange
function
(9)
under the constraints of Eq. (7), where αi,μi are the non-negative Lagrange multipliers.
The Karush–Kuhn–Tucker (KKT) conditions [16] for the primal problem are
(10)
(11)
(12)
(13)
ξi ≥ 0 (14)
αi ≥ 0, (15)
μ ≥ 0, (16)
(17)
µi ξi = 0 (18)
Hence,
(19)
We can use the KKT complementarily conditions, Eqs. (17) and (18), to determine b. Note
that Eq. (12) combined with Eq. (18) shows that ξj = 0 if αj < C. Thus we can simply take any
training data for which 0< αj < c to use Eq. (17) (with ξj = 0) to compute b.
b= yj – wT ᵠ(xj) (20)
It is numerically reasonable to take the mean value of all b resulting from such computing.
Hence,
(21)
where Ns is the number of the support vectors.
For a new data x, the classification function is then given by
f(x) = Sign(wT ᵠ(x) + b) (22)
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 73 http://www.euroasiapub.org
Substituting Eqs. (19) and (21) into Eq. (22), we get the final classification function
(23)
If there is a kernel function such that K(xi,xj)=ᵠ(xi)T ᵠ(xj), it is usually unnecessary to
explicitly Know what ᵠ(x) is, and we only need to work with a kernel function in the training
algorithm.
Therefore, the non-linear classification function is
(24)
Any function satisfying Mercer’s condition [17] can be used as the kernel function. In this
investigation, the radial kernel K(s, t) = exp(−1/10 ||s-t||2 ) is used as the kernel function of the
SVM because the radial kernel tends to give good performance under general smoothness
assumptions. Consequently, it is especially useful if no additional knowledge of the data is
available.
3. Experiment design
Several financial indicators are calculated in order to reduce dimensionality of the time
series:
: The relative price difference of prices p(t) at time t and p(t-1) at
time t-1
: The exponential moving average of the prices p(t)
: The relative strength indicator of the number of upward
movement U[t − n;t] and downward movements D[t − n;t] in the period of t-n until time t
: The stochastic indicator of the stock price p(t), lowest stock
price L[t − n;t] and highest stock price H[t − n;t] in the period of t-n until time t.
The figure illustrates some of the properties of the indicators derived as above from a random
time series. The kernel densities are estimated for each indicator with a bandwidth of 0.001.
Note, that the RDP and EMA indicator are rather gaussian distributed, while the RSI and
Stochastic indicators have several modes and especially the Stochastic indicator seems to be a
mixture of two different gaussian distributions.
In our empirical analysis, we set out to examine the weekly changes of the BSE30 Index. The
BSE30 Index is calculated and disseminated. It measures the composite price performance of
225 highly capitalized stocks trading on the Bombay Stock Exchange (BSE), representing a
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 74 http://www.euroasiapub.org
broad cross-section of Indian industries. Trading in the index has gained unprecedented
popularity in major financial markets around the world. Futures and options contracts on the
BSE30 Index are currently traded on the Securities and Exchange Board of India (SEBI), the
National Stock Exchange (NSE). The increasing diversity of financial instruments related to
the BSE30 Index has broadened the dimension of global investment opportunity for both
individual and institutional investors. There are two basic reasons for the success of these
index trading vehicles. First, they provide an effective means for investors to hedge against
potential market risks. Second, they create new profit making opportunities for market
speculators and arbitrageurs. Therefore, it has profound implications and significance for
researchers and practitioners alike to accurately forecast the movement direction of BSE30
Index.
3. MODEL INPUTS SELECTION Most of the previous researchers have employed multivariate input. Several studies have
examined the cross sectional relationship between stock index and macroeconomic variables.
The potential macroeconomic input variables which are used by the forecasting models
include term structure of interest rates (TS), short-term interest rate (ST), long term interest
rate (LT), consumer price index (CPI), industrial production (IP), government consumption
(GC), private consumption (PC), gross national product (GNP) and gross domestic product
(GDP). However, Indian interest rate has dropped down to almost zero since 1990. Other
macroeconomic variables weekly data are not available for our study.
Indian consumption capacity is limited in the domestic market. The economy growth has a
close relationship with Indian export. The largest export target for India is the United States
of America (USA), which is the leading economy in the world. Therefore, the economic
condition of USA influences Indian economy, which is well represented by the BSE30 Index.
As the BSE30 Index to Indian economy, the S& P 500 Index is a well-known indicator of the
economic condition in USA. Hence, the S& P 500 Index is selected as model input. Another
import factor that affects the Indian export is the exchange rate of US Dollars against Indian
Rupee (Rs), which is also selected as model input. The prediction model can be written as the
following function:
Directiont = F(St-1 S&P500 , St-1 IND), (25)
where St-1 S&P500 and St-1 IND are first order difference natural logarithmic transformation to
the raw S& P 500 index and IND at time t−1, respectively. Such transformations implement
an effective detrending of the original time series.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 75 http://www.euroasiapub.org
Fig. 1. First-order difference natural logarithmic weekly prices of BSE Index, S& P 500 Index. (observations from October
2010 to September 2011).
Directiont is a categorical variable to indicate the movement direction of BSE30 Index at time t.
If BSE30 Index at time t is larger than that at time t − 1, Directiont is 1. Otherwise, Directiont is −1.
The above model inputs selection is only based on a macroeconomic analysis. As shown in Fig. 1, the
behaviours of the BSE30 Index, the S& P 500 Index are very complex. It is impossible to give an
explicit formula to describe the underlying relationship between them.
3.1. Data collection
We obtain the historical data from the finance section of Yahoo and the Bombay Stock
Exchange and National Stock Exchange respectively. The whole data set covers the period
from January 1, 2007 to December 31, 2010, a total of 694 pairs of observations. The data set
is divided into two parts. The first part (652 pairs of observations) is used to determine the
specifications of the models and parameters. The second part (42 pairs of observations) is
reserved for out-of-sample evaluation and comparison of performances among various
forecasting models.
3.2.Comparisons with other forecasting methods
To evaluate the forecasting ability of SVM, we use the random walk model (RW) as a
benchmark for comparison. RW is a one step ahead forecasting method, since it uses the
current actual value to predict the future value as follows:
yˆt+1=yt, (26)
where yt is the actual value in the current period t and yˆt+1 is the predicted value in the next
period.
We also compare the SVM’s forecasting performance with that of linear discriminant
analysis (LDA), quadratic discriminant analysis (QDA) and Elman backpropagation neural
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 76 http://www.euroasiapub.org
networks (EBNN). LDA can handle the case in which the within class frequencies are
unequal and its performance has been examined on randomly generated test data. This
method maximizes the ratio of between-class variance to the within-class variance in any
particular data set, thereby guaranteeing maximal separability.
QDA is similar to LDA, only dropping the assumption of equal covariance matrices.
Therefore, the boundary between two discrimination regions is allowed to be a quadratic
surface (for example, ellipsoid, hyperboloid, etc.) in the maximum likelihood argument with
normal distributions. In this chapter, we derive a linear discriminant function of the form:
L(St-1 s&p500, St-1 IND) = a0 + a1 St-1 s&p500 + a2 St-1 IND) (27)
and a quadratic discriminant function of the form:
Q((St-1s&p500,St-1
IND) = a + P((St-1s&p500,St-1
IND)T+ ((St-1s&p500,St-1
IND)T((St-1s&p500, St-1 IND)T,
(28)
where a0, a1, a2, a, P,T are coefficients to be estimated.
Elman Backpropagation Neural Network is a partially recurrent neural network. The
connections are mainly feed forward but also include a set of carefully chosen feedback
connections that let the network remember cues from the recent past. The input layer is
divided into two parts: the true input units and the context units that hold a copy of the
activations of the hidden units from the previous time step. Therefore, network activation
produced by past inputs can cycle back and affect the processing of future inputs.
3.3. A combining model
Given a task that requires expert knowledge to perform, k experts may be better than one if
their individual judgments are appropriately combined. Based on this idea, predictive
performance can be improved by combining various methods. Therefore, we propose a
combining model by integrating SVM with other classification methods as follows:
(29)
where wi is the weight assigned to classification method i, We would like to
determine the weight scheme based on the information from the training phase. Under this
strategy, the relative contribution of a forecasting method to the final combined score
depends on the in sample forecasting performance of the learned classifier in the training
phase. Conceptually, a well-performed forecasting method should be given a larger weight
than the others during the score combination. In the investigation, we adopt the weight
scheme as follows:
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 77 http://www.euroasiapub.org
(30)
where ai is the in sample performance constructed by forecasting method i.
Table 1 Forecasting performance of different classification methods
Classification method Hit ratio (%)
RW 50
LDA 55
QDA 69
EBNN 69
SVM 73
Combining model 75
Table 2
Covariances matrices of input variables when Directiont = -1
SINDt-1 St-1
S&p500
SINDt-1 0.00015167706 0.00002147347
St-1 S&p500 0.00002147347 0.00044862762
4. EXPERIMENT RESULTS Each of the forecasting models described in the last section is estimated and validated by in
sample data. The model estimation selection process is then followed by an empirical
evaluation which is based on the out-sample data. At this stage, the relative performance of
the models is measured by hit ratio. Table 1 shows the experiment results.
RW performs worst, producing only 50% hit ratio. RW assumes not only that all historic
information is summarized in the current value, but also that increments–positive or
negative—are uncorrelated (random), and balanced, that is, with an expected value equal to
zero. In other words, in the long run there are as many positive as negative fluctuations
making long term predictions other than the trend impossible.
SVM has the highest forecasting accuracy among the individual forecasting methods. One
reason that SVM performs better than the earlier classification methods is that SVM is
designed to minimize the structural risk, whereas the previous techniques are usually based
on minimization of empirical risk. In other words, SVM seeks to minimize an upper bound of
the generalization error rather than minimizing training error. So SVM is usually less
vulnerable to the over fitting problem.QDA out performs LDA in term of hit ratio, because
LDA assumes that all the classes have equal covariance matrices, which is not consistent with
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 78 http://www.euroasiapub.org
the properties of input variable belonging to different classes as shown in Tables 2 and 3. In
fact, the two classes have different covariance matrices. Heteroscedastic models are more
appropriate than homoscedastic models.
The integration of SVM and the other forecasting methods improves the forecasting
performance. Different classification methods typically have access to different information
and therefore produce different forecasting results. Given this, we can combine the individual
forecaster’s various information sets to produce a single superior information set from which
a single superior forecast could be produced. Table 3 : Covariances matrices of input variables when Directiont = 1
SINDt-1 St-1
S&p500
SINDt-1 0.00018240800 -0.00002932242
St-1 S&p500 -0.00002932242 0.00044571885
The method of support vector regression includes several parameters to be chosen, which can
e.g. optimized using cross validation.
These parameter include the chosen kernel with parameter γ, the e of the e-insensitive loss
function, the cost of error c and the number of training samples. The advantage of using a
kernel is sometimes to be able to linearly classify inseparable cases like shown on the top of
the figure. In this case, the black and white label points on the left side are not linearly
separable. After the kernel transformation, however, the black and white labelled points
might fall onto the same point in the new space. Here, the classification problem becomes
trivial. Therefore choosing a kernel is high importance, as well as the parameter γ of the
kernel function.
Another parameter is the e of the insensitive loss function, which is illustrated on the bottom
of the figure. The support vector regression model is trained placing a penalty for values,
which are off target. The penalty depends on the e-insensitive loss function, with parameter e.
The idea is to penalize values off target only if the difference is higher than the absolute value
of e.
Given the kernel K(xi,xj) = φ(xi)Tφ(xj), the training set of instance-label pairs (xi,yi),i = 1,...,l,
where xi ϵ Rn and yi ϵ 1, -1l , the optimization problem of the support vector machines can
be formulated as min subject to yi (wT Ø (xi) + b) ≤ 1- Ei, Ei ≤ 0.
The support vector machine then maximizes the margin of the separating hyperplane of the
classes, which is equal to minimizing |w|/|t| and therefore also to minimizing |w|2 / 2.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 79 http://www.euroasiapub.org
Cross-validation
Figure 4: Cross-validation setup. Several parameter values are tested in the prediction accuracy on a training set,
of which then the optimal parameter combination is chosen for further prediction on the test set. Since the SVR parameters can be easily controlled manually, the optimal set of parameters is
chosen on a test set and then used on the following training set. The cross-validation is
applied as illustrated in the figure. The total data set is divided into two parts, one for cross-
validation and one for testing. A third part of the data set in order to optimize the structure of
the model, like the used indicators, is omitted in this study.
In order to optimize the number of training samples, the cost of error c, the kernel parameter
and the parameter e of the e-insensitive loss-function, a k-fold cross-validation is used as
follows: the dataset is divided into k folders of equal size; subsequently, a model is built on
all possible (k) combinations of k-1 folders, and each time the remaining one folder is used
for validation. The best model is the one that performs best on average over the k validation
folders. The benefit of using a cross-validation procedure is that by construction it ensures
that model selection is based entirely on out-of-sample rather than in-sample performance.
Thus, the search for the best Support Vector Regression model is immune to a critique of
drawing conclusions about the merits of a factor model based on its in-sample performance.
In this study, a 10-fold cross-validation procedure was used for each parameter above. In
each validation loop, different values for each parameter are chosen, while the other
parameters are set constant. Then the SVR model is trained with this set of parameters and
the prediction accuracy is calculated. This is done for all parameter combinations and then
the combination with the maximal prediction accuracy chosen.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 80 http://www.euroasiapub.org
Basic model
Figure 5: The basic model. The machine is trained on the past values of the indicators. The resulting model is
used to predict the movement on the next day (= 108 data points). After that the model is shifted and proceeds
again.
The basic simulation consists of two steps: First, at month t, all historical values for all
explanatory factors together with the difference in returns for the period’s t - n1 till t - 1 are
used to build numerous support vector regressions. Thus the dependent variable is the return
of the stock in the period of t till t + n2. The variable n2 is arbitrarily chosen to 108, in order
to decrease calculation time. The independent variables are the technical indicators as
described above.
Second, once the prediction is calculated, the model is shifted 108 data points and the model
is build again in order to predict the next 108 stock price movements.
Using only historically available data ensures the implementation of the trading strategy is
carried out without the benefit of foresight, in the sense that investment decisions are not
based on data that have become available after any of the to-be-predicted periods. Moreover,
investment decisions for the to-be-predicted months are always based on the entire factor set
of historical data, ensuring that no variable-selection procedures based on extensive
manipulation of the whole available data have been carried out. At any rate, the utilized
cross-validation procedure for model selection ensures that the best candidate model is
selected on the basis of performance in the training set and not on the basis of performance
on external validation samples.
Results and discussion
The data set consists of 5 minute closing prices p(t) for 28 stocks in the BSE Sensex. The
missing stocks are Satyam and Hypo Real Estate due to data unavailability. With a time
frame of nearly 7 years between April 2004 and August 2010, the data set comprises 140.000
data points per stock.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 81 http://www.euroasiapub.org
From this data set, the log return of each stock i is calculated as
with price p(t) at time t as well as the market average as
over all stocks i. From this the log return above market is calculated as
xi'(t) = xi(t) − xmarket(t) for each stock i.
Cross-validation
Several parameter values are chosen for each of the machine parameters. The cost and
training length parameter show linear dependencies, while the kernel parameter gamma
shows a quadratic dependency. The e parameter is rather nonlinear dependent to the
prediction accuracy. Several parameter conditions were tested on the first half of the data set.
The figure shows the tested values for each parameter. The optimality criterion used here, is
the cumulated return. Therefore, the model is trained with the parameter set, the prediction
calculated and then the return resulting from the prediction is cumulated over time. The
parameter values are tested on half of the data set, that is between May 2009 and July 2011.
On the top left of the figure, the results for different parameter values of the cost function is
shown. With an increasing cost function value, the cumulated return increases. This seems
plausible, since with an increasing cost the model is trained longer. However, the parameter
exploration is stopped at a cost value of 1000, since higher values increase computation time
dramatically.
The top right of the figure shows different parameter values for e of the e-insensitive loss-
function. Here the results seem to be rather nonlinearly related to the cumulated return, since
with increasing parameter e, the cumulated return decreases only in general. However,
generally, smaller values of e seem to be more successful. Since this value controls the
penalty of the training algorithm, a small value indicates a fast penalty for values off-target.
The kernel parameter gamma, plotted for different values on the bottom left, seems to
approach an optimum value around 1. The parameter controls the shape of the kernel. With
high parameter values, the kernel becomes rather flat and the model increasingly predicts
future movements only linearly, which is obviously insufficient. With small parameter
values, the kernel becomes very thin and training data are increasingly over fitted with
decreasing generalization performance. This again results in a low prediction
performance.Last, with an increasing number of training points the prediction performance
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 82 http://www.euroasiapub.org
increases. Therefore the quality of the trained model increases with the number of training
samples.
Prediction accuracy
The optimized parameters were tested with the basic model approach described above on the
second half of the data set. The prediction accuracy over all 28 stocks reached a mean of
50.69 percent with standard deviation of .26%. With this performance, the reported approach
significantly outperformed a random prediction approach. Even if a gain of .69 percent might
be a valuable trading prediction, this approach is market neutral and operated only on the
basic statistical properties of market movements.
5. CONCLUSIONS In this Chapter, we study the use of support vector machines to predict financial movement
direction. SVM is a promising type of tool for financial forecasting. As demonstrated in our
empirical analysis, SVM is superior to the other individual classification methods in
forecasting weekly movement direction of BSE30 Index. This is a clear message for financial
forecasters and traders, which can lead to a capital gain. However, each method has its own
strengths and weaknesses. Thus, we propose a combining model by integrating SVM with
other classification methods. The weakness of one method can be balanced by the strengths
of another by achieving a systematic effect. The combining model performs best among all
the forecasting methods.
The underlying time series were derived from the Bombay Stock Exchange Index. The
support vector machine was then trained in order to predict the movement of 28 stocks of the
index against market. Features for training were directly extracted from the statistical
properties of the time series and no fundamental information was used.
The model selection was based on the performance on out-of-sample data, in order to avoid
critique of foresight and was performed as cross-validation. The main result of this study is
that the movement of stocks is significantly predicted only using technical indicators with
support vector regression.
6. REFERENCES 1. Cristianini N, Taylor JS. An introduction to support vector machines and other kernel-
based learning methods. New York: Cambridge University Press; 2000.
2. Cao LJ, Tay FEH. Financial forecasting using support vector machines. Neural
Computing Applications 2001;10: 184–92.
IJREAS Volume 1, Issue 4 (December 2011) ISSN: 2294-3905
International Journal of Research in Engineering & Applied Sciences 83 http://www.euroasiapub.org
3. Tay FEH, Cao LJ. Application of support vector machines in financial time series
forecasting. Omega 2001;29:309–17.
4. Castanias R.P. Macro information and the Variability of Stock Market Prices. Journal
of Finance 34 (1979), pp.439–
5. Schwert G William. The Adjustment of Stock Prices to Information about Inflation.
The Journal of Finance 36 (1981), pp.15-29.
6. Schwert G William. Stock Returns and Real Activity: A Century of Evidence. Journal
of Finance 14 (1990), pp.1237-1257.
7. Fama EF. Stock Returns, Real Activity, Inflation and Money. American Economic
Review 71 (1981), pp.71:545
8. Nai-Fu Chen, Roll R, Ross R. Economic Forces and The Stock Market. Journal of
Business 59 (1986), pp.383-403.
9. Hardouvelis Gikas A. Macroeconomic Information and Stock Prices. Journal of
Economics and Business 1987;39:131-140.
10. Darrat AF. Stock Returns, Money and Fiscal Deficits. Journal of Financial and
Quantitative Analysis 25 (1990), pp.387–98.
11. Blank SC. Chaos in futures market? a nonlinear dynamical analysis. Journal of
Futures Markets 1991;11:711–28.
12. DeCoster GP, Labys WC, Mitchell DW. Evidence of chaos in commodity futures
prices. Journal of Futures Markets 1992;12:291–305.
13. Frank M, Stengos T. Measuring the strangeness of gold and silver rates of return. The
Review of Economic Studies 1989;56:553–67.
14. Frank M, Stengos T. Measuring the strangeness of gold and silver rates of return. The
Review of Economic Studies 1989;56:553–67.
15. Vapnik VN. Statistical learning theory. New York: Wiley; 1998.
16. Vapnik VN. An overview of statistical learning theory. IEEE Transactions of Neural
Networks 1999;10:988–99.