Natarajan Venkat Kumar, Samson Mathew, Ganapathiram Swaminathan

Abstract: This paper describes the utility of fuzzy Simulink model to assess the groundwater quality levels in Tiruchirappalli city, S. India. Water quality management is an important issue in the modern times. The study aimed at examining the influence of multiple parameters of ground water on potable quality. The data collected for Tiruchirappalli city have been utilized to develop fuzzy Simulink approach. This is illustrated with seventy nine groundwater samples collected from Tiruchirappalli corporation, S. India. The characteristics of the groundwater groundwater for this plain were monitored during the years 2006 and 2008. The quality of groundwater at several established stations within the plain were assessed using Fuzzy simulation. The results of the calculated fuzzy logic Simulink model and the monitoring study have yielded good agreement. Groundwater quality for potability indicated high to moderate water pollution levels at Srirangam, Ariyamangalam, Golden Rock and K. Abisekapuram depending on factors such as depth to groundwater, constituents of groundwater and vulnerability of groundwater to pollution. Fuzzy logic simulation approach was a practical, simple and useful tool to assess groundwater quality. This approach was capable of showing the water quality assessment for drinking on fuzzy Simulink model

Keywords: Groundwater, Potability, Fuzzy Simulation, GIS, Tiruchirappalli

Cite this paper: nullN. Kumar, S. Mathew and G. Swaminathan, "Multifactorial Fuzzy Approach for the Assessment of Groundwater Quality," Journal of Water Resource and Protection, Vol. 2 No. 6, 2010, pp. 597-608. doi: 10.4236/jwarp.2010.26069.

References

[1]   Bureau of Indian Standard, “Indian Standard Specification for Drinking Water,” BIS Publication No. IS (10501), New Delhi, 1991.

[2]   World Health Organization, “Guidelines for Drinking Water Quality Recommendation,” Vol. 2, World Health Organization, Geneva, 2008.

[3]   M. F. Dahab, Y. W. Lee and I. Bogardi, “A Rule Based Fuzzy-Set Approach to Risk Analysis of Nitrate Contaminated Groundwater,” Water Sciences Technology, Vol. 30, No. 7, 1994, pp. 45-52.

[4]   B. Dixon, “Prediction of Groundwater Vulnerability Using an Integrated GIS Based Neuro-Fuzzy Techniques,” Journal of Spatial Hydrology, Vol. 4, No. 2, 2004, pp. 38-41.

[5]   R. Khaiwal and V. K. Garg, “Distribution of Fluoride in Groundwater and its Suitability Assessment for Drinking Purposes,” International Journal of Environmental Hea- lth Research, Vol. 16, No. 2, 2006, pp. 163-166.

[6]   K. Schulz and B. Howe, “Uncertainty and Sensitivity Analysis of Water Transport Modeling in a Layered Soil Profile Using Fuzzy Set Theory,” Journal of Hydroin-formatics, Vol. 1, No. 2, 1999, pp. 127-138.

[7]   B. Dixon, H. D. Scott, J. C. Dixon and K. F. Steele, “Prediction of Aquifer Vulnerability to Pesticides Using Fuzzy-Rule Based Models of the Regional Scale,” Physical Geography, Vol. 23, 2002, pp. 130-152.

[8]   B. Dixon, “Application of Neuro-Fuzzy Techniques in Predicting Groundwater Vulnerability: A GIS Based Sensitivity Analysis,” Journal of Hydrology, Vol. 309, No. 1-4, 2005, pp. 17-38.

[9]   S. M. Preveena, M. H. Abdullah, A. Z. Aris and K. Bidin, “Groundwater Solution Techniques: Environmental Ap-plications,” Journal of Water Resource and Protection, Vol. 2, 2010, pp. 8-13.

[10]   Z. K. Sen, “Fuzzy Groundwater Classification Rule Derivation from Quality Maps,” Water Quality Exposure Health, Vol. 1, No. 1, 2009, pp. 115-112.

[11]   American Public Health Association, “Standard Method for Examination of Water and Waste Water,” 21st Edition, American Public Health Association, Washington, DC., 2005.

[12]   B. Shomar, S. A. Fkher and Alfred Yahya, “Assessment of Groundwater Quality in the Gaza Strip, Palestine Using GIS Mapping,” Journal of Water Resource and Protection, Vol. 2, No. 2, 2010, pp. 93-104.

[13]   V. Kumar, N. S. Mathew and G. Swaminathan, “Fuzzy Information Processing for as Assessment of Groundwater Quality,” International Journal of Soft Computing, Vol. 4, No. 1, 2009, pp. 1-9.

[14]   Z. Chen, G. H. Huan and A. Chakma, “Hybrid Fuzzy-Stochastic Modeling Approach for Assessing Environmental Risks at Contaminated Groundwater Systems,” Journal of Environmental Engineering, Vol. 129, 2003, pp. 79-88.

[15]   S. Liou and S. A. L. Wang, “Generalized Water Quality Index for Taiwan,” Environmental Monitor Assessment, Vol. 9635-52, 2004.

[16]   S. Liou and S. L. Lo, “A Fuzzy Index Model for Tropic Status Evolution of Reservoir Waters,” Water Research, Vol. 96, No. 1, 2004, pp. 35-52.

[17]   L. A. Zadeh, “Fuzzy Set,” Information Control, Vol. 8, No. 3, 1965, pp. 338-353.

[18]   L. A. Zadeh, ‘The Concept of a Linguistic Variable and Its Application to Approximate Reasoning,” Information Science, Vol. 8, 1975, pp. 199-249.

[19]   M. Jamshidi, “Tools for Intelligent Control: Fuzzy Controllers, Neural Networks and Genetic Algorithms,” Philosophical Transactions of the Royal Society, Vol. 361, No. 1809, 2003, pp. 1781-1808.

[20]   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.

[21]   V. K. Garg, S. Dahiya, A. Chaudhary and Deepshikha “Fluoride Distribution in Underground Waters of Jind District, Haryana, India,” Ecology Environmental Con-servation, Vol. 4, No. 172, 1998, pp. 19-23

[22]   E. M. Mamdani, “Advances in the Linguistic Synthesis of Fuzzy Controllers,” International Journal of Man-Machine Studies, Vol. 8, No. 6, 1976, pp. 669-678.

[23]   N. Chang, H. W. Chen and S. K. King, “Identification of River Water Quality Using the Fuzzy Synthetic Evaluation Approach,” Journal Environmental Management, Vol. 63, 2001, pp. 293-305.

[24]   C.O. Cude, “Water Quality Index: A Tool for Evaluation Water Quality Management Effectiveness,” Journal of the American Water Resources Association, Vol. 37, No. 1, 2001, pp. 125-137.