International Conference on Transport, Environment and ...psrcentre.org/images/extraimages/28...

Abstract— The role of Nitrate as one of the

qualitative parameters of groundwater and its sensitivity on social health requires accurate periodic measurement .The goal of this article is to introduce an efficient, precise and inexpensive method in comparison with the regression methods. This method is based on Artificial Intelligence. By extracting and using the quantitative and qualitative information of groundwater wells, we can estimate the values of nitrate. The groundwater qualitative data of Birjand plain were gathered from 35 wells and aqueducts twice a year (every 6 months) from 2008 until 2010. As a result the artificial neural network was optimized by genetic algorithm and can predict data with the correlation coefficient of 0.83 and gives values between lab results and actual results which shows reliability of this method for predicting nitrate values in Birjand plain. By drawing the distribution plot we can see the alteration of nitrate values in plain of Birjand during the study and get that the western parts are more sensitive to nitrate than other parts of plain.

Keywords— Groundwater, Nitrate, Artificial Neural Network,

Genetic Algorithm, Predict .

I. INTRODUCTION HE risk of environmental contamination resulting from intensive land pro-duction systems must be controlled. From an economic point of view, nutrient losses are

unfavorable and, concerning the agroecosystem, loading scan be noxious to neighboring compartments. Since more than 60% of phosphate and more than 90% of nitrate loading in German rivers originate from agriculture (Harenz et al., 1992), ground and surface waters are often loaded with agro- chemicals (Stachowicz et al., 1994). Aquifers are vulnerable to contamination by residential, agricultural, and industrial pollutants. Sources of ground water contamination are

1 MSc student in Irrigation and Drainage Department, University of Zabol, Zabol, Iran (Email : [email protected])

2 Assistant professor in Department of water and soil, University of Zabol, Zabol, Iran

3 MSc student in irrigation and drainage department ofScience and Research Branch of Islamic Azad University of Tehran ,Iran.

4 MSc student in Irrigation and Drainage Department, University of Zabol, Zabol, Iran

5 MSc student in Chemical engineering department of Sistan and Baluchestan University

widespread and include accidental spills, landfills, storage tanks, pipelines, and agricultural activities; among many other sources (Bedient et al., 1994). Of these sources, agriculture-related activities are well-known to cause non-point source pollution in small to large watersheds especially due to fertilizers and various carcinogenic substances found in pesticides ( [Jansen et al., 1999], [Wolf et al., 2003] and [Tianhong et al., 2003]). Nitrate (NO3) is the most common pollutant found in shallow aquifers due to both point and non-point sources (Postma et al., 1991). Nitrate is the primary nitrogen species lost from soils by leaching due to its high mobility ( [Hubbard and Sheridan, 1994], [Ling and El-Kadi, 1998], [DeSimone and Howes, 1998] and [Tesoriero et al., 2000]). The main contamination path way into surface waters is through soil-associated contaminants following erosion (Bari-sas et al., 1978), but also, subsurface direct flow inter flow during storms, accelerated by drain pipes, contributes to lateral input into small streams (Dils and Heath waite, 1998 ;Gachteretal., 1998). Lateral direct flow had been long under estimated (Morgenstern and Efimov, 1993; Schulein, 1998) and found to be crucial for nutrient losses in hilly regions with heterogeneous sediments, such as the Tertiary Hills covering approximately 30% of the arable land in Bavaria, Germany (Hantsche land Lenz, 1993). Elevated nitrate concentrations in drinking water are linked to health problems such as methemoglobinemia in infants and stomach cancer in adults ( [Lee et al., 1991], [Addiscott et al., 1991] and [Wolfe and Patz, 2002]). In the central part of the Tertiary Hills, at the Research Station Scheyern, land use was completely modified in order to reducec on tamination effects, namely, the eutrophication of surface waters and nitrate loading of groundwater reser- voirs. In 1992 and 1993, landscape management combined with sustainable farming practices according to organic and integrated farming guide lines were implemented by the interdisciplinary FAM Research Network. In order to control these efforts, an intensive monitoring program concerning water quality and material fluxes was started. The aim of thisstudy was to examine the temporal changes of nutrient concentrations in soil, surface and groundwater, focusing on the behavior of nitrate and phosphate. The relation between landuse, site conditions and water quality are discussed in terms of its impacton agricultural methods and future research needs. As such, the U.S. Environmental Protection Agency (US EPA) has

Estimating the Groundwater Nitrate by using Artificial Neural Network and Optimizing it

by Genetic Algorithm (Case Study: BIRJAND plain, SOUTHERN KHORASAN, IRAN)

Sayyed Ali Moasheri P0F

1P, Seyyed Mahmood Tabatabaie P1F

2P, Parinaz Razaghi P2F

3P,Noushin Sarani P3F

4P,

Seyyede Homa Eslami Mahdi Abadi P4F

5

T

International Conference on Transport, Environment and Civil Engineering (ICTECE'2012) August 25-26, 2012 Kuala Lumpur (Malaysia)

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established a maximum contaminant level (MCL) of 10 mg l−1 as NO3-N (U.S. Environmental Protection Agency, 2000). Many studies have shown that agricultural activities are the main source of elevated nitrate concentrations in ground water ( [Hudak, 2000], [Spalding et al., 2001], [Harter et al., 2002], [Spruill et al., 2002], [Johnsson et al., 2002], [Mitchell et al., 2003] and [Lake et al., 2003]). Agricultural practices can result in non-point source pollution of ground water (Sivertun and Prange, 2003).

Nowadays managements of natural resources is known as one of the most important prerequisite of sustainable development for all countries ,and especial attention should be given to water resources management. In a country like Iran with lack of precipitation and erratic distribution of surface resources, groundwater resources have especial importance. Birjand plain is located in southern Khorasan, Iran with coordinates of the northeast which have arid climate with low average of precipitation .As there is no permanent and semi-permanent rivers in this region, the groundwater resources play the main role in drinking, agriculture and industry. Therefore, protecting the groundwater in aspect of qualitative and quantitative aims is so important. All of protecting solution and actions in this field needs investigation on the changes and predicating the changes. According to the announcement of the United States Environmental Protection Agency, the bacteria and nitrate are tow materials that have standard critical limitations and exceeding this limitation cause serious problems. Nitrate and bacteria are two important polluting materials which are found in human and animal excreta. Sewage wells and septic tank can also cause bacterial and nitrate pollution in water as well as ranching (which keeps lots of animals).Not only the septic wells but also the sewage wells and biomass should be managed in order to reduce and prevent pollution .Places in which sewage and waste are gathered, can be the origin of pollution .Nitrogen is a negative ion (Anion) which blends with positive ions and becomes some sort of potassium nitrate or sodium nitrate salts. Nitrate is one of the most solvable Anions known. Nitrate is being used as sodium or potassium nitrate in fertilizers. It is a chemical material with the chemical formula of NO3- .Nitrate is the most oxidized form of nitrogen which is found in nature. Nitrate is a polluting material which can extremelyinfect the surface and ground water.

Nitrate is a potential threat especially for newborns because it can cause a disease called "Methemoglobini"or "Bruised baby syndrome ". When you swallow nitrate with your food or drink, it will convert to nitrite in intestine and then it will combine with hemoglobin in blood (Hemoglobin = the protein which carries oxygen and cause the red color of blood), and produce methemoglobin. As a result the ability to conveying oxygen in blood will reduce. The amount of nitrate in urban water and other public resources should be maintained near standards by regular

measurements and monitoring. If the applied resource's nitrate were above standards they should be refined. According to Standard No.1053 of Standard and Industrial Researches Institute of Iran the maximum allowed amount of nitrate and nitrite for drinking water especially for personal

and private wells are 50 and 3 respectively, and also the

ratio of density to the advised amount shouldn't be above 1.

Before all of qualitative controlling actions on groundwater we should do lots of researches on alteration of qualities of groundwater. In this article we predict the process of nitrate alteration which is an important parameter on water quality and water health, by using genetic algorithm and artificial neural network techniques.

A. Artificial Neural Network (ANN) Artificial neural network was introduced for the first time in

1943 by McCullouch and Pitts, but this method was useless for a long time until the computer development and the genesis of backpropagation training algorithm for forward networks in 1986 by David E. Rumelhart et al. ANN is a combination of parallel operations of simple elements. These elements were inspired by neural systems. ANN can be calibratedby adjusting the relation and weights between elements in order to run an operational function.Usually by using actual data and try to reduce the difference between ANN outputs and desired output, the ANN will be calibrated. The networks were usually balanced according to the differences and comparisons between the requested (aimed) outputs and the network outputs, and this will continue till the outputs match with goal. The main structure of ANNs had been inspired from the biological networks of human brain. ANNs are systems with the ability to calculatearithmetic operations like natural neural networks. The ANN structure is divided to "one layer networks" and "multilayer networks". The multilayer networks include 3 main layers: input layer, hidden or middle layer and output layer. The usual and practical models of ANN are MLP (Multi-LayeredPerceptrons) ,RBF (radial basis function network ) and GFF (generalized feed forward ) .These networks are of the most important networks in modeling the dynamic systems which have several implication on solving complicate problems .The data collection which is used as inputs of the network are divided into 2 main categories: 1)controlled data 2)uncontrolled data .Training the network by controlled data is used when the output vector had been defined . In this condition while the data are being trained, the correct answer will be shown in the feedback. In fact the training algorithm is trying to minimize the errors and the differences between


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output vectors. In MLP the error function is alternative to the weighting coefficients. Therefor; the error values should be calculated in the outer layers per unit first and thenfeedback them to the previous layers of network tochange the weighting coefficients and errors in the previous layers .Actually feeding back the errors in the previous layers is the cause of using "backpropagation training" in the networks. Training the network with uncontrolled data is used for clustering and in this method there is no feedback. In this training, the network is being trained to identify the resemblance between input vectors (different patterns) and reach the classification.

Fig 1. Simple mathematical model for natural nerve.

Performance of artificial neural network (ANN) : 1) Adjusting the weight values according to pairs of input and output 2) Starting the weights by random values 3) Loading the learned sample inputs 4) Getting the outputs of calculations 5) Modifying the weights to reduce the differences 6) Replication for all samples 7) Stopping when the changes in weights become constant and the errors become minimized.

B. Modeling according to ANN: The development and expansion of a model of ANN

requires exact definitions and design for its technical components. To reach the goals of different structures of ANN like Perceptrons, usually tried to choose and use the best network with computed values of error. Also to find the effective factor on nitrate the sensitivity analysis had been done.At last to choose the appropriate and optimized model, the correlation coefficient (R2) and MSE (mean squared error) had been used. Basically entering the data as unprepared and raw data will reduce the accuracy and speed of network. To prevent such problems and also matching the data values before training the neural network, the input data should become standard and optimized. Standardizing the input data will prevent the excessive reduction in weights. To design ANN models 3 groups of data are required: 1) Training data 2) validation data 3) Testdata. The training data are used to find the relations between inputs and observed outputs. The validation data are used to control and monitor the training procedure and the test data are used to evaluate the

performance of the suggested network. NeuroSolutions is the software that is used to model the ANNs.The above model has an input layer,a hidden layer and an output layer. This software has the power of optimization in order to solve the problems like predicting, classifying and estimating the functions. The normalized data in the interval of ( 0 , 1) are used to increase the speed and accuracy of ANNs performance.

C. Genetic Algorithm The genetic algorithms are following the Darwin's theory of

natural selection to find the optimized formula for predicting or matching the patterns. The genetic algorithms are usually a good choice for predicting technics based on regression. It has been shortly said that the genetic algorithm (GA) is a programing technic which has used the genetic elution as a solving pattern. The solutions for the input problems are being coded by a pattern which is called Fitness function, this function evaluate solutions in a random way. The genetic algorithm has some differences from classic methods of optimization, for example it starts from several points of collection simultaneously to look for the result instead of one point, which prevents the algorithm to locate on the local minimum and it doesn't need the differentiates. Therefore it doesn't contain complex calculations or additional calculations and it also use statistical rules instead of explicit rules. These points increase the power of the algorithm. In this study the genetic algorithm were used to train the neural networks and to optimize the results of neural network.

II. RESEARCH METHODOLOGY This research is based on the information from 142 wells

and aqueducts in the Birjand Plain which were being sampled twice a year in the period of 2008-2010. Gathered information includes Electrical connectivity (EC), PH, temperature, Hydraulic conductivity and nitrate amount.

Fig 2. schema of designed neural network

In the first step, data were being normalized and imported


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into the NeuroSolution software. Respectively, the amounts of 60, 15 and 25 percent of data were used for training, validation and test of the model. The input data include 4 low expense variables which don’t need laboratory processes for this model. MLP and GFF networks were used in this study and the transition functions were tanh with domain of R (real numbers) and the range of [0,1] and linear with domain of R (real numbers) and range of R (real numbers).

Fig 3. linearaxon and tanhaxon transition functions

Sensitivity Analysis: Sensitivity Analysis provides the model designer with

valuable information about the sensitivity of the model with regard to its input variables. By understanding the impact of input variables on the estimation precision of the model, low impact variables can be removed from the network and a simpler model can be developed. In this study, Statsoft method was used for sensitivity analysis. Values of sensitivity coefficients of input variables were calculated by dividing the errors of the network when one variable was omitted by the error of the network when no variable was omitted. In this method, if the value of the sensitivity coefficient becomes more that 1, the variable assumed to have a large impact in defining the dependent variable.

After training the network for all arrangements, the ANNs with different typologies and multi-variable regression were used to evaluate the efficiency of the model. On the other hand the statistics of coefficient, mean square error (MSE) and mean absolute error (MAE) were used to find the desirable number of repetition. These values were calculated respectively from these formulas:

(2)

(3)

(4)

In the above formulas, Xi and Yi are the ith measured and

estimated data and and are the averages of these data. (Xi) is the observed value in the ith point and (Xi) is the estimated value in the ith point and n is the number of the evaluation selection of Xi and Yi.

III. CONCLUSION AND DISCUSSION

To estimate the qualitative parameter of the Nitrate by use of Artificial Neural Network, input factors which were introduced to the network were EC, PH, temperature, Hydraulic conductivity and discharge (Q). All these parameters were measurable in the sampling area and the output factor was Nitrate. Designed functions in the Artificial Neural Network in this paper are shown in the table 1.

TABEL 1. SCHEMA OF SOME OF THE DESIGNED ANNS

After running different Artificial Neural Networks of GFF

and MLP with transition and hidden functions, statistical indexes of R2, MSE and MRE were calculated and the network was trained and tested by NeuroSolutions software. The results are presented in the table 2.

TABLE 2. STATISTICAL INDEXES OF R2, MSE AND MRE FOR ANNS Network index MAE MSE R2

A 0.621733 8.747161 0.73 B 0.596242 7.587653 0.81 C 0.586641 7.536302 0.80 D 0.655315 9.493223 0.69 E 0.648783 9.28079 0.64 F 0.608286 8.058492 0.77 G 0.640737 8.041088 0.66 H 0.626342 7.666036 0.76

According to table 2, the results of running the network

with transition functions and different hidden layers indicate that the MLP network with 2 hidden layers and Tanaxon activation function with R=0.81 and MSE=7.587653 (Network B) has a higher correlation coefficient. This correlation coefficient determines the capability of the network in developing the relationship between introduced inputs and corresponding outputs in the network. Here, training of the network B is optimized by the use of Genetic Algorithm and the results show a significant improvement.

Network index network

Number of

hidden layers

First activatio

n function

Second activatio

n function

Third activatio

n function

Output function

A MLP 1 Tan - - Linear B MLP 2 Tan Linear - Linear C MLP 3 Tan Linear linear Linear D GFF 1 Tan - - Linear E GFF 1 Linear - - Tan F MLP 1 Linear - - Tan G GFF 2 Tan Linear - Linear H GFF 3 Tan linear Linear Linear


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TABLE 3. OPTIMIZED STATISTICAL INDEXES OF R2, MSE AND MRE FOR ANNS BY GENETIC ALGORITHM

Network index MAE MSE R2

BGA 0.54213 7.324716 0.83 Figures 4 to 12 represent the measured and estimated amounts of Nitrate by ANN in the Birjand Plain.

Fig 4. comparison of nitrate estimation process, neural network BGA

Fig 5. comparison of nitrate estimation process, neural network

A

Fig 6. comparison of nitrate estimation process, neural network B

Fig 7. comparison of nitrate estimation process, neural network C

Fig 8. comparison of nitrate estimation process, neural network D

Fig 9. comparison of nitrate estimation process, neural network E.

Fig 10. comparison of nitrate estimation process, neural network F.

Fig 11. comparison of nitrate estimation process, neural network G.


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Fig 12. comparison of nitrate estimation process, neural network H

Among the tested networks and their corresponding

outcomes, the BGA with R=0.83 and MSE=7.324716 has the best results with regard to estimating the amount of Nitrate in the Birjand plain. Considering the facts that all the factors introduced in this Neural Network are measurable in the sampling area and the results in the paper, Neural Networks can be known as low cast and fairly reliable method to estimate the amount of Nitrate in Birjand plain.

The trend of outcome changes in the testing phase, are shown point by point in the figure 12.This figure indicate the correlation between real amount of Nitrate and estimated amounts by ANN in the testing phase. Congestion of samples in the regression line indicates the acceptable correlation of this test.

Fig 13. Effectiveness of input factors on nitrate values.

Further analysis about the sensitivity of the results against

the input factors are done on the results of ANN estimation for the best network. Therefore, to determine the sensitivity and impact of different input factors, MLP network with 1 hidden layer and tanaxon activation function and be used. Sensitivity degree or impact of each of the input factors on the outcomes of the network are presented in the below network:

TABLE 3. SENSITIVITY ANALYSIS FOR INPUT PARAMETERS OF THE NETWORK ON THE NITRATE RESULTS

Sensitivity Nitrate (NO32-) %

EC 62.52185332 PH 28.69899969

Temperature 6.271476298 Discharge 2.507670685

Fig 14. Effectiveness of input factors on nitrate values

It can be seen that EC > pH > T > Q. It means EC is the most influential factor and pH, temperature and Hydraulic conductivity and discharge (Q) are in the next levels. Figure 14 shows how Nitrate amount changes against changes in EC, pH, T and Q parameters it actually shows the positive and negative impacts of input factors on nitrate values.


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Fig 15. the positive and negative impacts of input factors on nitrate values analyses.

Finally, figure 16 to 19 show the drawn maps of geographical distribution of Nitrate amount.

Fig 16. maps of geographical distribution of Nitrate amount in the second half of 2008.

Fig 17. maps of geographical distribution of Nitrate amount in the first half of 2009.

Fig 18. maps of geographical distribution of Nitrate amount in the second half of 2009

Fig19. maps of geographical distribution of Nitrate amount in the first half of 2010.

As it can be observed in geographical distribution maps of Nitrate amount in the period of 2008 to 2010, the Nitrate amount of groundwater is in the acceptable range. Trend of changes in Nitrate amount shows a low pace increase toward the North-East of the plain. Increasing use of Nitrate fertilizers by farmers and annual precipitations intensify the risk of the nitrate increases in the underground water in these areas.

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Seyyed Ali Moasheri was born in Birjand on 23th January 1988. He is a MSc student in Irrigation and Drainage Department, University of Zabol, Zabol, in Iran. Author of more than 8 scientific articles. Expert in the field of artificial neural networks and ArcGIS and Geostatistical and Groundwater quality.


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