Identification of critical areas of groundwater quality in Golestan province using fuzzy clustering method

Document Type : Complete scientific research article

Authors

1 M.Sc. Student of Water Resource Engineering, Faculty of Water and Soil, University of Zabol, Iran.

2 Corresponding Author, Associate Prof., Gorgan University of Agricultural Sciences and Natural Resources - University of Zabol, Iran.

3 M.Sc. Graduate of Water Resource Engineering, University of Tehran, Iran.

4 Instructor, Dept. of Water Engineering, Faculty of Water and Soil, University of Zabol, Iran.

Abstract

Background and Objectives: One of the most important crises that most countries are currently ‎facing is the issue of reducing the quality of water resources. Reducing groundwater resources and ‎increasing pollution, reduced the potential for using groundwater for various uses. One of the main ‎reasons for the decline in groundwater quality is the impact of agricultural drainage due to the ‎excessive use of fertilizers and pesticides. Also, industries that in many cases have contaminated ‎groundwater with chemical and hydrocarbon contaminants.‎‏ ‏In addition to these factors, the disposal ‎of sewage in cities and villages through absorbent wells made up of viruses and bacteria also ‎contributes to contaminants‏.‏‎ Groundwater quality monitoring program can ensure the proper quality ‎of water resources for different uses‏.‏‎ Without monitoring, continuous reporting on the quality of ‎the water supply, its evolution, planning for optimal allocation for different uses, assessing the ‎impact of new developments, and designing and implementing management plans is not feasible.‎‏ ‏Identification of homogeneous regions in terms of groundwater quality in Golestan province of ‎northern Iran using Fuzzy Clustering Method combination with genetic algorithm (GA-FCM) was ‎performed on 14 parameters in a 5- year- time step in 2006, 2011 and 2016.‎
Material and Methods: To determine the homogeneous regions for each year, the optimal number ‎of clusters was initially obtained. After data clustering in Matlab software, the results of clustering ‎were evaluated qualitatively with Schuler and Wilcox diagrams. For better representation of ‎homogeneous regions, classification maps for the study area were presented.‎
Result and discussion: The results showed that the optimum numbers of clusters in 2006, 2011, ‎and 2016 were 6, 5, and 6, respectively. Analysis of groundwater quality classification maps ‎showed that in 2006, cluster no. 6, including 2.7% of the studied wells located within the city of ‎Kalaleh, is poor in terms of drinking and farming groundwater quality. Also, based on the results, it ‎can be seen that 36.8% of the wells across the province were in good condition in terms of quality ‎of drinking and agricultural parameters in 2011. Likewise, 33.33% of the wells are in a moderate ‎condition in terms of drinking quality, and the status of their groundwater has improved in terms of ‎quality since 2006. Also, the results of NSGA- FCM in 2016 showed that most of the parameters ‎‎(5.55% of the wells in the province) in the cluster 3 have a moderate quality.‎
Conclusion: The findings of this study showed that the groundwater quality in the province in ‎‎2016 is lower than in 2011, so appropriate management plans should be adopted. Moreover, it was ‎observed that the fuzzy clustering method is a suitable method for assessing and identification of ‎critical region of the quality of groundwater resources, since it considers the uncertainty conditions ‎in the classes of the classification system.‎

Keywords

References

1.Ahani, A., and Mousavi Nadoushani, S. 2016. Assessment of some combinations of hard and fuzzy clustering techniques for regional station of catchments in Sefidroud basin. Journal of Hydroinformatics. 18: 1033-1054.
2.Bezdek, J.C., Chuah, S.K., and Leep, D. 1986. Generalized K-nearest neighbor rules. Fuzzy Sets and System. 18: 237-256.
3.Bezdek, J.C., Ehrlich, R., and Full, W. 1984. FCM: the fuzzy c-means Clustering algorithm. Computers and Geosciences. 10: 2. 191-203.
4.Bricker, O.P., and Jones, B.F. 1995. Main factors affecting the composition of natural waters, in CRC Press, Boca Raton, pp. 1-5.
5.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.
6.Goyal, M.K., and Gupta, V. 2014. Identification of Homogeneous Rainfall Regimes in Northeast Region of India using Fuzzy Cluster Analysis. Water Resources Management, 28: 13. 4491-4511.
7.Guler, C., Thyne, G.D., McCray, J.E., and Turner, A.K. 2002. Evaluation of graphical and multivariate statistical methods for classification of water chemistry data, Hydrogeology Journal, 10: 455-474.
8.Guler, C., and Thyne, G.D. 2004. Delineation of hydrochemical facies distribution in a regional groundwater system by means of fuzzy c-means clustering. Water Resources Research.40: 2345-2360.
9.Guler, C., Kurt, M., Alpasalan, M., and Akbulut, C. 2012. Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques, Journal of Hydrolog. 414-415: 435-451.
10.Kalantari, N., Rahimo, M., and Akbari, A. 2009. Hydrochemical study of Mianaab plain using Statistical method, hydrochemical diagrams and fuzzy logic, Iranian Journal of Geology.3: 9. 15-25. (In Persian)
11.Kathy, P., World Health Organization. Water, Sanitation and Health Team. 2005‎. Water recreation and disease : plausibility of associated infections: acute effects, sequelae and mortality, 239p.
12.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: 6. 733-742.
13.Ott, W.R. 1978. Water quality indices: A survey of indices used in the United States, Office of Monitoring and Technical Support, Office of Research and Development, U.S. Environmental Protection Agency; Available to the public through National Technical Information Services. EPA-600/4-78-005.
14.Sivasankar, V., Kameswari, M., Msagati, T.A.M., Venkarapathy, M., and Senthil Kumar, M. 2013. Fuzzy set approach-A tool to cluster Holy samples of groundwater quality parameters at Rameswaram South India, Journal of Water Resources and Ocean Science.2: 3. 33-39.
15.Zadeh, L.A. 1965. Fuzzy Sets. Information control, 8: 338-353.
16.Zou, H., Zou, Z., and Wang, X. 2015. An Enhanced K-Means Algorithm for Water Quality Analysis of the Haihe River in China, International Journal of Environmental Research and Public Health, 12: 11. 14400-14413.
Journal of Water and Soil Conservation
Volume 28, Issue 4
March 2022
Pages 167-186

Files

Share

How to cite

Statistics