Recognition and Spatial Mapping of Multivariate Groundwater Quality Index using Combined Fuzzy Method

Document Type : Research Paper


1 Ph.D. Student of Water Resources, Dept. of Civil Eng., Tehran University

2 Assoc. Prof. of Civil Eng., Dept. of Civil Eng., Sharif University of Tech., Tehran

3 Grad. Ph.D. of Civil and Water Eng., Dept. of Eng., Tehran University, Tehran

4 M.Sc. of Water Resources Management, Tehran Regional Water Company, Tehran


Methods for evaluatingthe quality of groundwater resources and recognition of appropriate locations for urban and agricultural water demand has been known as an important item in water resources planning. The main focus of this paper has been addressed a new water quality mapping based on coupling of fuzzy approximation and water quality indexing. In this paper, after indexing water quality in each monitoring well, based on fuzzy reasoning a new global fuzzy indexing has been presented. In this regard, 12 water quality parameters for 217 monitoring wells in the province of Ghazvin in the north have been used. In the final step, map of fuzzy evaluation over the area of interest has been provided based on an optimized new spatial approximation via Genetic Algorithm (GA). The results showed the capability of the proposed methodology for groundwater quality mapping. The most important contribution of this paper is successful combination of fuzzy reasoning and water quality indexing as a continuous function for evaluating groundwater quality mapping.


1- WHO. (1984). Guidelines for drinking water quality recommendation, Vol. II, World Health Organization, Geneva.
2- De Zuane, J. (1996). Handbook of drinking water quality, 2nd Ed., John Wiley and Sons Inc., New york.
3- Razaghi, N., Hejazi, M., Shokouhi, Sh., Goudarzi, H., and Mashhoun, A. (1991). Method for examination of drinking water, 2nd Ed., ISIRI Pub., Tehran. (In Persian).
4- Ott, W.R. (1978). Environmental indices, theory and practice, Ann Abbor. Science Pub., Michigan.
5- Ott W.R. (1978). Water quality indices: A survey of indices used in the United States, EPA-600/4-78-005, US Environmental Protection Agency, Washington, DC.
6- Horton, R.K. (1965). “An index number system for rating water quality.” J. of Water Pollut. Control Fed., 37(3), 300-305.
7- Cude, C.O. (2001). “Water quality index: A tool for evaluating water quality management effectiveness.” J. of Am. Water Resour. Assoc., 37, 125-137.
8- Liou, S., Lo, S., and Wang, S.A. (2004). “Generalized water quality index for Taiwan.” J. of Environ. Monit. Assess., 96, 35-52.
9- Said, A., Stevens, D., and Selke, G. (2004). “An innovative index for evaluating water quality in streams.” J. of Environ. Manage, 34, 406-414.
10- Abbasi, S.A. (2002). “Water quality indices, state of the art, centre for pollution control and energy technology.” < Quality Indices.doc> (Sep. 12, 2010)
11- Joung, H.M. (1978). A water quality index based on multivariate factor analysis, University of Nenada, Reno, NV.
12- Harkins, R.D. (1974). “An objective water quality index.” J. of Water Poll. Control Fed., 46(3), 588-591.
13- Schaeffer, D.J., and Konanur, G.J. (1977). “Communicating environmental information to the public: A water quality index.” J. of Environ. Educ., 8(4), 18-26
14- Sarbu, C., and Pop, H.F. (2005). “Principal component analysis versus fuzzy principal component analysis, a case study: The quality of Danube water (1985-1996).” J. of Talanta, 65, 1215-1220.
15- Pop, H.F., Einax, J.W., and Sarbu, C. (2009). “Calssical and fuzzy principal component analusis of some environmental samples concerning the pollution with heavy metals.” J. of Chemometrics and Intelligent Laboratory Systems, 97, 25-32.
16- Luukka, P. (2009). “Classification based on fuzzy robust algorithms and similarity classifier.” Expert systems with applications, 36, 7463-7468.
17- Kung, H.T., Ying, L.G., and Liu, Y.C. (1992). “A complementary tool to WQI: Fuzzy clustering analysis.” J. of Water Resources Bulletin, 28(2), 525-533.
18- Sii, H.I., Sherreard, J.H., and Wilson, T.E. (1993). “A water quality index based on fuzzy sets theory.” Proc. of the 1993 Joint ASCE-CSCE National Conference on Environmental Engineering, Montreal, Quebec, Canada, 253-259.
19- Silvert, W. (2000). “Fuzzy indices of environmental conditions.” J. of Ecological Modeling, 130, 111-119.
20- Ocampo-Duque, W., Ferre-Huguet, N., Domingo, J.L., and Schuhmacher, M. (2006). “Assessing water quality in rivers with fuzzy inference systems: A case study.” J. of Environment International, 32, 733-742.
21- Dahiya, S., Singh B, Gaur, S, Garg, V.K, and Kushwaha, H.S. (2007). “Analysis of groundwater quality using fuzzy synthetic evaluation.” J. of Hazardous Materials, 147, 938-946.
22- Singh, B., Dahiya, S., Jain, S., Garg, V.K, and Kushwaha, H.S. (2008). “Use of fuzzy synthetic evaluation for assessment of groundwater quality for drinking usage: A case study of Southern Haryana, India.” J. of Environmental Geology, 54, 249-255.
23- Abkhan Consulting Engineers. (2008). Detail studies of surface and groundwater resources of Ghazvin open land, Water Resources Management, Terhran Province Water Corp., Tehran. (In Persian)
24- Critto, A., Carlon, C., and Marcomini, A. (2003). “Characterization of contaminated soil and groundwater surrounding and illegal landfill (S. Giuliano, Venice, Italy) by principal component analysis and kriging.” J. of Environmental Pollution, 122(2), 235-244.
25- Marinoni, O. (2003). “Improving geological models using a combined ordinary-indicator kriging approach.” J. of Engineering Geology, 69, 37-45.
26- Jang, C., Chen, S.K., and Chieh, L.C. (2008). “Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the choushui river alluvial fan.” J. of Hydrological Processes, 22, 4477-4489.
27- Oyedele, D.J., Amusan, A.A., and Obi, A.O. (1996). “The use of multiple-variable indicator kriging technique for assessment of the suitability of an acid soil for maize.” J. of Tropical Agriculture, 73(4), 259-263.
28- Juang, K.W., and Lee, D.Y. (1998). “Simple indicator kriging for estimating the probability of incorrectly delineating hazardous areas in a contaminated site.” J. of Environmental Science and Technology, 32, 2487-2493.
29- Lyon, S.W., Lembo, A.J., Walter, M.T., and Steenhuis, T.S. (2006). “Defining probability of saturation with indicator kriging on hard and soft data.” J. of Advanced Water Resources, 29, 181-193.
30- Liu, C.W., Jang, C.S., and Liao, C.M. (2004). “Evaluation of arsenic contamination potential using indicator kriging in the Yun-Lin aquifer (Taiwan).” J. of Science of the Total Environment, 321, 173-188.
31- Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G., and Nriagu, J. (2005). “Geostatistical modeling of the spatial variability of arsenic in groundwater of Southeast Michigan.” J. of Water Resources Research, 41, 13-25.
32- Abedini, M.J., and Nasseri, M. (2008). Inverse distance weighted revisited, 4th Ed., APHW, Beijing, China.
33- Abedini, M.J, Nasseri, M., and Burn, D. (2012). “The use of a genetic algorithm-based search strategy in geostatistics: Application to a set of anisotropic piezometric head data.” J. of Computers and GeoSciences, 41, 136-146.
34- Ebrahimi, R., Zahraie, B., and Nasseri, M. (2011). “Mid-term prediction of meteorological drought using fuzzy inference systems.” J. of Water and Wastewater, 78, 112-125, (In Persian).