Analysis of Groundwater for Potability from Tiruchirappalli City Using Backpropagation ANN Model and GIS
Natarajan Venkat Kumar, Samson Mathew, Ganapathiram Swaminathan
DOI: 10.4236/jep.2010.12018   PDF    HTML     7,085 Downloads   12,297 Views   Citations


Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination due to point sources and non point sources. This paper presents Artificial neural Network (ANN) Models that might be used to predict water parameters from a few known parameters. The sample data from 112 hand pumps and hand operated tube well water samples used for drinking purposes by the local population was used. The ANN model features a back propagation algorithm and neuron members were determined for optimization of the model architecture by trial and error method. The model simulations show that the optimum network of 4-50-50-6 has mean error of –0.023% on complete data was utilized. This demonstrated that the developed model has high accuracy for predicting. Thus it has been established that the two hidden layers neural network has more efficiency than asymptotic regression in the present. This model can be used for analysis and prediction of subsurface water quality prediction.

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Kumar, N. , Mathew, S. and Swaminathan, G. (2010) Analysis of Groundwater for Potability from Tiruchirappalli City Using Backpropagation ANN Model and GIS. Journal of Environmental Protection, 1, 136-142. doi: 10.4236/jep.2010.12018.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] American Public Health Association, “Standard Method for Examination of Water and Waste Water,” 21st Edition, American Public Health Association, Washington, D.C., 2005.
[2] Bureau of Indian Standard, “Indian Standard Specification For Drinking Water,” BIS Publication No. IS: 10501, New Delhi, 1991.
[3] Z. Chen, G. H. Huan, A. Chakma, “Hybrid Fuzzy-Stochastic Modeling Approach for Assessing En-vironmental Risks at Contaminated Groundwater Sys-tems,” Journal of Environmental Engineering, Vol. 129, No. 1, 2003, pp. 79-88.
[4] C. Okoliand and S. D. Pawlowski, “The Delphi Method as a Research Tool an Example, Design Considerations and Applications,” Information and Management, Vol. 42, No. 1, 2004, pp. 15-29.
[5] R. D. Deshpande and S. K. Gupta, “Water for India in 2050: First Order Assessment of Available Options,” Current Science, Vol. 86, No. 9, 2004, pp. 1216-1224.
[6] T. Subramani, L. Elango and S. R. Damodarasamy, “Groundwater Quality and its Suitability for Drinking and Agricultural Use in Chithar River Basin, Tamil Nadu, In-dia,” Environmental Geology, Vol. 47, No. 8, 2005, pp. 1099-1110.
[7] N. V. Kumar, S. Mathew and G. Swaminathan, “A Pre-liminary Investigation for Groundwater Quality and Health Effects—A Case Study,” Asian Journal of Water, Environment and Pollution, Vol. 5, No. 4, 2008, pp. 99- 107.
[8] K. Sivasankar and R. Gomathi, “Fluoride and Other Quality Parameters in the Groundwater Samples of Pet-taivaithalai and Kulithalai Areas of Tamil Nadu, Southern India,” Water Quality Exposure Health, Vol. 1, No. 2, 2009, pp. 123-134.
[9] R. Khaiwal and V. K. Garg, “Distribution of Fluoride in Groundwater and its Suitability Assessment for Drinking Purposes,” International Journal of Environmental Health Research, Vol. 16, No. 2, 2006, pp. 163-166.
[10] N. V. Kumar, S. Mathew and G. Swaminathan, “Fuzzy Information Processing for Assessment of Groundwater Quality,” International Journal of Soft Computing, Vol. 4, No. 1, 2009, pp 1-9.
[11] S. Dahiya, B. Singh, S. Gaur, V. K. Garg and H. S. Kushwaha, “Analysis of Groundwater Quality Using Fuzzy Synthetic Evaluation,” Journal of Hazard Materials, Vol. 147, No. 3, 2007, pp. 938-946.
[12] World Health Organisation, “Guidelines for Drinking Water Quality Recommendation,” Vol. 2, World Health Organisation, Geneva, 1984.
[13] Z. Sen, “Fuzzy Groundwater Classification Rule Deriva-tion from Quality Maps,” Water Quality Exposure Health, Vol. 1, 2009, pp. 115-112.
[14] W. C. Chen, G. L. Fu, P. H. Tai and W. J. Deng, “Process Parameter Optimization for MIMO Plastic Injection Molding via Soft Computing,” Expert System with Ap-plications, Vol. 36, No. 2, 2009, pp. 1114-1122.
[15] H. Demuth and M. Beale, “Neural Network Toolbox User’s Guide,” Version 4 (Release 12), The Mathworks, Inc., 2000.

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