Application of Soft Computing Techniques for Analysis of Groundwater Table Fluctuation in Bangkok...

*Corresponding author (Dr. Uruya Weesakul). Tel/Fax: +66-2-5643001 Ext.3189. E-mail addresses: [email protected] 2010. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642 Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf

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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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Application of Soft Computing Techniques for Analysis of Groundwater Table

Fluctuation in Bangkok Area and Its Vicinity Uruya Weesakula*, Kunio Watanabeb, and Natkritta Sukasemc a Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILAND b Geosphere Research Institute, Saitama University, JAPAN c Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILAND A R T I C L E I N F O

A B S T RA C T

Article history: Received 1 August 2010 Received in revised form 20 September 2010 Accepted 27 September 2010 Available online 12 October 2010 Keywords: Groundwater; Artificial Neural Network (ANN); Genetic Algorithm (GA)

Being a good quality water resource, groundwater was over used during the last three decades to serve high water demand due to rapid growth in Bangkok and its vicinity. Excessive pumping rate of groundwater in Bangkok results in land subsidence problem and groundwater quality deterioration due to saltwater intrusion into shallow aquifers adjacent to the coast. This study applied a simple linear Genetic Algorithm (GA) model as an alternative tool for monitoring and forecasting of groundwater table. Nonthaburi aquifer, one of three major aquifers amongst seven aquifers in greater Bangkok area, was analyzed in the study. Monthly groundwater table of 43 monitoring wells, amongst 92 wells, 12 years (1997-2009) data was analyzed with land use map. GA was used to divide groundwater basin into sub-regions. Comparison between capability of GA and Artificial Neural Network (ANN) models for prediction of groundwater level reveals that ANN model has a better performance for all cases. However, GA model might be used to predict groundwater level with an acceptable accuracy (9% to 26% relative error). Better performance was obtained in medium to high residential area and industrial area (9-19% relative error). Due to its simplicity as well as period of record length of data requirement, GA is another appropriate alternative tool for monitoring and forecasting groundwater table fluctuation particularly for insufficient data area.

2010 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Some Rights Reserved.

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

54 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem

1. Introduction Rapid growth of Bangkok and its vicinity in population, business, industries and tourism

results in increasing in water demand dramatically. Groundwater, as another good quality water

resources was over-abstraction during the last three decades in order to fulfill such high

requirement. Excessive pumping rate of groundwater in Bangkok and its adjacent 6 provinces

area (Nonthaburi, Pranakhon Si Ayutthaya, Patumthani, Samut-Prakan, Samut-Sakorn and

Nakhonpatom so called, Greater Bangkok area) results in land subsidence problem (AIT, 1982)

as well as groundwater quality deterioration due to saltwater intrusion into shallow aquifers

adjacent to the coast (Ramnarong, 1983 and Ramnarong, 1991).

Several studies were conducted in order to investigate appropriate measurement to alleviate

such problems, for example: mitigation of groundwater crisis and land subsidence in Bangkok

(Ramnarong and Buapeng, 1991), groundwater resources of Bangkok and its vicinity: impact and

management of groundwater and land subsidence in the Bangkok Metropolitan area and its

vicinity (JIGA, 1995) and groundwater impact beneath a major metropolis: the Bangkok

experience (Ramnarong, 1996) etc. A number of attempt were implemented in order to remedy

the problems such as control of groundwater use (mainly in the critical zone) to reduce

groundwater abstraction since 1983, effective use of groundwater Act of 1977 (since June 1978)

and enforcement of groundwater charges policy since 1985, (Ramnarong, 1999). Due to such

measurement and policy, presently, the groundwater situation in greater Bangkok seems to be

gradually recovered (Limskul and Koontanakulvong, and Phien-wej et. al, 2006). Particularly,

the strict policy on pricing measures in the year 2003 can alleviate over-abstraction problem

resulting in gradually increasing in groundwater level in greater Bangkok area (as shown in

Figure 6). However, it is still necessary to monitor and forecast fluctuation of groundwater level

for management and warning system.

Several methods were proposed and manipulated for monitoring system for groundwater

resources management in the area. For example, the three-dimensional groundwater flow model

(MODFLOW) and the one-dimensional consolidation model were successfully coupled and

calibrated to simulate the piezometric levels and land subsidence in the Bangkok aquifer system.

MODFLOW results can replicate the observed amount and variation of piezometric levels and

land subsidence better than the quasi 3-D model results (AIT, 1998). Artificial Neural Network

(ANN) model was applied to monitor groundwater level for management system in greater


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Bangkok area, the results reveal that ANN can be applied very well to interpret artificial effect

and natural effect to groundwater system, therefore, it is very appropriate tool for monitoring and

management environmental and engineering problem (Watanabe and Weesakul, 2004).

However, it seems that the various models already developed require either a number of data

or mathematical skill for complicate manipulation, it is interesting to try to use a simple linear

Genetic Algorithm (GA) model requiring only monthly data with short term record length (less

than 10 years record) to analyzed and forecast a fluctuation of groundwater table in Bangkok

area. Therefore, this study tries to propose a simple linear Genetic Algorithm (GA) model to

apply to monitor and forecast fluctuation of groundwater table in Bangkok area and its vicinity,

as another alternative tool for groundwater resources monitoring and management system.

2. Study Area and Data Collection

2.1 Study Area Bangkok has no distinctive geological feature. The area consists entirely of alluvial deposits,

which accumulated during the Pleistocene period until the present day. It consists of very fine-

grained sediment mainly grayish or brownish clay forming a very thick layer with some silt, sand

or gravel lens. The deposits replenished every year by flooding of the Chao Phraya river. The

land is somewhat flatten with the elevation averaging around 1-2 metres above MSL. The

deposition took place somewhere around 25 million years ago and was part of the main central

flood plain regime of Thailand. Groundwater trapped in void between gravel and sand grains of

flood plain and lower terrace deposits, consisting of multiple aquifers from the depth of 40

meters. These aquifers are underlain and overlain by layer of relatively impermeable clays and

typically known as confined aquifer. Water quality is normally suitable for drinking as well as

household and industrial usages except in some areas and some aquifers, locally.

The ground surface of Bangkok is entirely underlined by blue to gray marine clay, 15-30

metres in thickness, known as the Bangkok Clay. Unconsolidated and semi-consolidated

sediments overlying the basement have a total thickness of about 400 metres to more than 1,800

metres. From detailed study of logs of groundwater wells, Department of Mineral Resources

(DMR) identified and named eight aquifers within 550 metres depth. These aquifers consist


mainly of sand and gravel separated by clay beds. Details of these aquifers are as shown in Table

1.

Table 1 Aquifers in Bangkok and its vicinity

Aquifer name Thickness

(m)

Depth from ground elevation

(m)

Bangkok 30 16-30

Phra Pradaeng 20-50 60-80

Nakhon Luang 50-70 100-140

Nonthaburi 30-80 170-200

Sam Khok 40-80 240-250

Phaya Thi 40-60 275-350

Thonburi 50-100 350-400

Amongst these aquifers, Pha Pradaeng (PD), Nakhon Luang (NL) and Nonthaburi (NB)

aquifer are extensively utilized due to their availability of amount of water as well as their good

quality. According to availability of groundwater table data, and present extensively use,

groundwater from Nonthaburi (NB) aquifer was selected to be analyzed in this study.

2.2 Data Collection The groundwater monitoring network in Bangkok was firstly established in 1987 under the

comprehensive study program on groundwater and land subsidence. The network was aimed at

monitoring potentionmetric and water quality in the three main aquifers of Phra Pradaeng (PD)

Nakhon Luang (NL) and Nonthaburi (NB). A network of groundwater monitoring system

consists of 279 monitoring wells, with 93 wells for PD, 94 wells for NL and 92 wells for NB.

Groundwater table data from 92 wells of NB aquifer were collected in the study. Preliminary

analysis of data reveals that only monthly data was recorded and some stations were just

implemented for few years. Based on availability of data, only 43 monitoring wells were selected

for further analysis in this study. Figure 1 shows distribution of location of these wells over

landuse map of greater Bangkok area. Landuse map in 2007 was collected and used in the further

clustering analysis.


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Figure 1: Location of monitoring wells on land use map of greater Bangkok area.

3. Analysis of Groundwater Table Fluctuation

3.1 Analysis of Correlation between Monitoring Wells An agglomerative procedure was adopted in the study in order to investigate correlation of

groundwater table fluctuation between monitoring wells so that the similar behavior of

fluctuation can be grouped together. The result of analysis through correlation matrix reveals that

monitoring wells can be roughly grouped into 3 categories. The first group (7 wells) has low

correlation with correlation coefficient less than 0.9. The second group (26 wells) has medium

correlation with correlation coefficient between 0.9 and 0.95. The last group (16 wells) has high

correlation with correlation coefficient greater than 0.95. Table 2 shows classification of these

monitoring wells in each group.

Kilome


Table 2 Classification of monitoring wells based on correlation coefficient and type of landuse

Correlation

coefficient

Landuse type

<0.90 0.90 0.95 >0.95

Low density NB61,NB86,NB88, NB02,NB35,NB46,

residential area NB89,NB90,NB91, NB47,NB64,NB82 -

and agricultural area NB92

Medium density

residential area

NB24,NB38,NB58,NB63,


- NB50,NB51,NB55,NB62, -

NB81,NB87

High density

residential area

and industrial area




- - NB59,NB66,NB76,NB83

3.2 Clustering by Landuse Type In order to be able to describe different behaviour of fluctuation of groundwater table in

different groups (as shown in Table 2). Landuse type was introduced to investigate locations of

wells in each group. It has been found that pattern of fluctuation of groundwater table in

agricultural area is less correlated to each other since use of groundwater for agricultural

purposes depends on amount of rainfall related to variation in climate situation. However, for

medium density to low density residential area, fluctuation of groundwater table has higher

correlation than agricultural area (0.90≥ρ≤0.95), since water supply system from surface water is

quite well distributed and behaviour of water use in the area is more predictable. The highest

correlation between wells was found in high density residential area and industrial area where

behavior of water use is quite certain and predictable. Table 2 shows classification of group of

wells based on type of landuse.


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4. Division of Groundwater Flow Subbasin Using GA Model Genetic algorithms (GA) is traditionally a procedure for operational similarities with the

biological and behavioral phenomena of living beings. In the last decade a flourishing literature

has been devoted to their application to real problems, after the pioneering work by John Holland

(1975). The basic of the method can be found in Goldberg (1989). Various application can be

found in Chambers (1995).

Figure 2: Groundwater flow sub-basin for low density residential area and agricultural area with

low correlation coefficient (ρ<0.9).

It is interesting to use GA model as a tool to describe groundwater flow region resulting in

division of groundwater flow sub-basin. Groundwater monitoring wells in each category as

shown in Table 2 were analyzed by using GA model. Each monitoring well in each group (Table

2) was then tested as a target well to be predicted by its neighboring wells with in the same

group. The resulted weighted coefficients (α) in linear equation of GA were used as indicator to

group monitoring wells within the same sub-basin. After successive processes of GA, for all

wells in each category, division of groundwater flow sub-basins can be identified as shown in

Figures 2 to 5.

Kilomete


Figure 3: Groundwater flow sub-basins for low density residential area and agricultural area with

high correlation coefficient ( >0.9).

Figure 4: Groundwater flow sub-basins for medium density residential area.

Kilometers.

Kilometres


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Fig. 5. Groundwater flow sub-basins for high density residential area and industrial area

5. Forecasting of Groundwater Table Fluctuation Using GA and ANN

Models

In order to test capability of GA model for forecasting groundwater table fluctuation, GA

model was used to analyze fluctuation of groundwater table fluctuation of each monitoring wells

in each sub-basin (as shown in Figures 2 to 5) by using monthly groundwater data from 1997 to

2003 (7 years) as calibration period. Then monthly groundwater data from 2004 to 2009 (6 years)

was used for testing of performance of GA model in forecasting fluctuation of groundwater table.

Relative error between forecasted and observed groundwater table was adopted as indicator to

evaluate performance of model. ANN model was also used in the same manor for the purpose of

comparison with GA model. Figure 6 illustrates an example of results by comparison between

observed and forecasted groundwater table by GA and ANN models at monitoring well located at

Chatu Chak district, Bangkok (industrial area). It reveals that performance of GA model in

forecasting fluctuation of groundwater table is not much difference from ANN model. Table 3

Kilomete


summarizes the results of all cases, it has been found that ANN model can predict groundwater

table better than GA model for all cases, with average relative error of 9.64% for ANN model and

average relative error of 15.37% for GA model. However, considering simplicity of GA model

and short-term data record length requirement, GA model is an appropriate alternative tool for

forecasting groundwater table with acceptable accuracy, particularly for insufficient groundwater

data area.

Table 3: Comparison of performance of GA and ANN models in forecasting fluctuation of

groundwater table.

Landuse type Monitoring well

Relative error (%)

GA model ANN model

Calibration Prediction Calibration Prediction

1997-2003 2004-2009 1997-2003 2004-2009

Low density NB88,NB89,NB90, 18.32 26.41 9.65 17.49

residential NB91,NB92

area and

agricultural

area NB35,NB46,NB47,9.08 11.67 4.28 8.64

<0.90 NB82

Medium NB24,NB38,NB58, 5.63 9.17 3.05 6.51

density NB63,NB65

residential area NB30,NB50,NB55, 10.98 13.98 2.79 5.16

0.90≤ρ≤0.95 NB81,NB62

High density NB11,NB27,NB32, 10.66 19.74 6.8 11.79

residential NB42,NB59

area and

industrial area NB53,NB54,NB66, 8.4 11.3 4.59 8.25

>0.95 NB76

Average 10.51 15.37 5.19 9.64


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Figure 6: Comparison between observed and forecasted groundwater table by GA and ANN

models at Chatu Chak, Bangkok (NB0042).

6. Conclusion A simple linear Genetic Algorithm (GA) model was proposed to be used as another

alternative tool for monitoring and forecasting fluctuation of groundwater table in Bangkok area

its vicinity. Nonthaburi aquifer, one of three major aquifers amongst seven aquifers in greater

Bangkok area, was analyzed in the study. Monthly groundwater table of 43 monitoring wells

amongst 92 wells in the area, during 12 years (1997-2009) was analyzed with landuse map. GA

was used to divide the area into sub-regions of groundwater basin. Comparison between

capability of GA and ANN models reveals that ANN model has a better performance for all

cases. However, GA model can be used to predict groundwater level with an acceptable accuracy

(with 9% to 26% relative error). Better performance was obtained in medium to high residential

area and industrial area (9%-19% relative error). Due to its simplicity as well as short period of

record length of data requirement, GA is another appropriate alternative tool for monitoring and

forecasting groundwater table fluctuation particularly for insufficient data area.

-36

-34

-32

-30

-28

-26

-24

-22

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year

Gro

undw

ater

leve

l de

pth

from

ass

umed

gr

ound

ele

vatio

n(m

.)

ObservedGA modelANN model

Calibrat Predi


7. Acknowledgement This study was supported by the research collaboration between Saitama University and

Thammasat University under the International Collaborative Graduate Program on Civil and

Environmental Engineering (ICGP). Groundwater data was kindly provided by Department of

groundwater resources. All these supports are gratefully acknowledged.

8. References Asian Institute of Technology. Investigation of land subsidence caused by deep well pumping in

the Bangkok area, phase IV : extension of subsidence observation network ; Research report. Division of deotechnical and transportation engineering. Thailand 1982

Asian Institute of Technology. FEM quasi-3D modeling of responses to artificial recharge in the Bangkok multiaquifers system. Environmental modeling and software 1998; 14: 141-151.

Chambers L. Practical Handbook of Genetic algorithms,Vols. 1 and 2.CRC Press. 1995

Department of Mineral Resources (DMR). Groundwater resources in the Bangkok area: development and management study comprehensive report. Nation environment broad Bangkok Thailand 1982

Goldberg D.E. Genetic algorithms in search. Optimization and machine learning. Addison-Wesley 1989

Holland J.J. Adaptation in natural and artificial systems. University of Michigan Press. Ann Arbor, MI .1975

Japan International Cooperation JICA. The study on management of groundwater and land subsidence in the Bangkok metropolitan area and its vicinity. Report submitted to Department of Mineral Resources and Public Works Department, Kingdom of Thailand 1995; 1-1 -11-5.

Limskul K. Koontanakulvong S. Groundwater pricing in greater Bangkok area. Water resources systems research unit, Faculty of engneering, Chulalongkorn university.Thailand. 2004

Phien-wej N. Land subsidence in Bangkok Thailand. Engneering geology 2006; 82: 187-201.

Ramnarong, V. and Buapeng, S. Groundwater resources of Bangkok and its vicinity impact and management. Proceedings of a national conference on geologic resources of Thailand potential for future development Bangkok, Thailand 1992; 2: 172-184.

Ramnarong,V. and Buapeng S. Mitigation of Groundwater crisis and land subsidence in Bangkok: J. Thai geosciences. 1991; 2: 125-137.

Ramnarong V. Evaluation of groundwater management in Bangkok: positive and negative. Groundwater in urban environment: Department of mineral resources, Bangkok, Thailand 1999


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Ramnarong V. Groundwater depletion and land subsidence in Bangkok. Proceedings of conference on geology and mineral resources of Thailand, Department of mineral resources, Bangkok, Thailand 1983

Ramnarong, V. Groundwater impact beneath a major metropolis: the Bangkok experience. Proceedings of inaugural conference on groundwater and land-use planning, Fremantle, Australia 1996; 107-117.

Watanabe K. and Weesakul U. Hydrological monitoring system based on the ANN: Application to the groundwater management, Proceedings of the 9th nation convention on civil engineering,Thailand 2004; INVITED-1-6.

Dr. Uruya Weesakul is Associate Professor at the Department of Civil Engineering, Faculty of Engineering, Thammasat University. She received her B.Eng. (Civil Engineering) with Honors from Khonkhen University, Thaialand. She received M.A. (Water resources Engineering) from Asian Institute of Technology (Thailand). Also, she focused on remote sensing and gained M.A. (Remote sensing) GDTA , Toulouse (France). Later, she received her PhD (Mechanical and Civil Engineering) from University of Montpellier II (France). Her current research interests involve hydrological process in tropical southeast Asian area. Currently, Dr. Uruya Weesakul is the Dean of the Faculty of Engineering, Thammasat University, Thailand.

Dr. Kunio WATANABE is Professor of the Geosphere Research Institute, Saitama University, Japan. He received D.Eng. from University of Tokyo. He was JICA Expert at Thammasat University, Thailand (1997-1998). Dr. WATANABE is specialized in ground water engineering, ground environmental engineering, and geology.

Natkritta Sukasem was a graduate student at the Department of Civil Engineering, Faculty of Engineering, Thammasat University. She received her B.Eng. from Kasetsart Univesity, Thailand. She is interested in analysis of groundwater table fluctuation.

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