Review of groundwater quality monitoring network by combining clustering and geostatistical methods in Tehran-Karaj study area

Document Type : Research Paper


1 M.Sc. Student, Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.

2 Assistant Professor, Department of Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.

3 Associate Professor, Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.


Protecting the quantity and quality of water resources has always been of great importance in all human societies, and in order to maintain the quality of these resources, numerous monitoring and remedial measures have been taken in most countries of the world. In this regard, water quality monitoring is considered as one of the essential tools and as an integrated activity to evaluate the physical, chemical and biological factors of water that are related to human health and living organisms. In the present study, a model of combining geostatistical methods (Kriging), clustering and entropy theory has been proposed to review and present the groundwater quality monitoring network. This model is presented under the first and second approaches in Tehran-Karaj study area. The first approach, without using the clustering method, reviews the existing monitoring network without using only entropy theory and Kriging geostatistical method as an estimator. The second approach uses the k-means clustering method, entropy theory and Kriging geostatistical method as an estimator to investigate the effect of combining these three methods on the review of the existing monitoring network and then the results of the first and second approaches are compared. The proposed final monitoring network with 44 wells has an average forecast error rate of 19 and a cost reduction of 34 percent compared to the cost of the current monitoring network. Also, using clustering, the average percentage of estimation error has been reduced by 20 percent compared to the case without clustering.


Main Subjects

  1. Ahani, A., & Mousavi Nadoushani, S. S. (2014). Regionalization of Aras Watershed by SOFM. Iran-Water Resources Research, 10(3), 88-98. (In Persian).
  2. Ahmadi, S.H., & Sedghamiz, A. (2007) Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level. Environmental Monitoring and Assessment, 129, 277-294.
  3. Alfonso, L., Lobbrecht, A., & Price, R. (2010). Optimization of water level monitoring network in polder systems using information theory. Water Resources Research, 46(1), 1-13.
  4. Alfonso, L., He, L., Lobbrecht, A., & Price, R. (2013). Information theory applied to evaluate the discharge monitoring network of the Magdalena River. Journal of Hydroinformatics, 15(1), 211-228.
  5. Alilou, H., Moghaddam Nia, A., Keshtkar, H., Han, D., & Bray, M. (2018). A cost-effective and efficient framework to determine water quality monitoring network locations. Science of the Total Environment, 624, 283-293.
  6. Alizadeh, Z., Yazdi, J., & Moridi, A. (2018). Development of an Entropy Method for Groundwater Quality Monitoring Network Design. Environmental Processes, 5(4), 769-788.
  7. Boroumand, A., Rajaee, T., & Masoumi, F. (2018). Semivariance analysis and transinformation entropy for optimal redesigning of nutrients monitoring network in San Francisco bay. Marine Pollution Bulletin, 129(2), 689-694.
  8. Chang, C. L., & Lin, Y. T. (2014). A water quality monitoring network design using fuzzy theory and multiple criteria analysis. Environmental Monitoring and Assessment, 186(10), 6459-6469.
  9. Daughney, C. J., Raiber, M., Moreau-Fournier, M., Morgenstern, U., & van der Raaij, R. (2012). Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: Comparison of New Zealand’s national and regional groundwater monitoring programs. Hydrogeology Journal, 20(1), 185-200.
  10. Du, X., Shao, F., Wu, S., Zhang, H., & Xu, S. (2017). Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Environmental Monitoring and Assessment, 189(7).
  11. Esquivel, J. M., Morales, G. P., & Esteller, M. V. (2015). Groundwater Monitoring Network Design Using GIS and Multicriteria Analysis. Water Resources Management, 29(9), 3175-3194.
  12. Hosseinimarandi, H., Mahdavi, M., Ahmadi, H., Motamedvaziri, B., & Adelpur, A. (2014). Assessment of Groundwater Quality Monitoring Network Using Cluster Analysis, Shib-Kuh Plain, Shur Watershed, Iran. Journal of Water Resource and Protection, 06(06), 618-624.
  13. Janatrostami, S., & Salahi, A. (2020). Design of the optimal groundwater quality monitoring network using a genetic algorithm based optimization approach. Environmental Sciences, 18(2), 19-40. (In Persian).
  14. Karamouz, M., Ahmadi, A., & Akhbari, M. (2020). Groundwater Hydrology: Engineering, Planning, and Management (2nd ed.). CRC Press.
  15. Komasi, M., & Goudarzi, H. (2021). Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (case study: Silakhor plain). Journal of Hydroinformatics, 23(1), 136-150.
  16. Li, H., Wang, D., Singh, V. P., Wang, Y., Wu, J., & Wu, J. (2021). Developing an entropy and copula-based approach for precipitation monitoring network expansion. Journal of Hydrology, 598(November 2020), 126366.
  17. Masoumi, F., & Kerachian, R. (2008). Optimal groundwater monitoring network design using the entropy theory. of Water and Wastewater, 65, 2-12. (In Persian).
  18. Mogheir, Y., Singh, V. P., & De Lima, J. L. M. P. (2006). Spatial assessment and redesign of a groundwater quality monitoring network using entropy theory, Gaza Strip, Palestine. Hydrogeology Journal, 14(5), 700-712.
  19. Rajaee, T., Masoumi, F., & Ahmadi Siavoshani, F. S. (2021). Optimal location of water quality monitoring stations in river systems by discrete transinformation entropy. Iranian Journal of Irrigation & Drainage, 15(2), 295-306. (In Persian).
  20. Rezaei, F., Safavi, H. R., & Ahmadi, A. (2013). Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Environmental management, 51(1), 267-277.
  21. Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
  22. Singh, V. P. (1997). The use of entropy in hydrology and water resources. Hydrological processes, 11(6), 587-626.
  23. Taheri, K., Missimer, T. M., Amini, V., Bahrami, J., & Omidipour, R. (2020). A GIS-expert-based approach for groundwater quality monitoring network design in an alluvial aquifer: a case study and a practical guide. Environmental Monitoring and Assessment, 192(11).
  24. Xiong, H., Wu, J., & Chen, J. (2006). K-means clustering versus validation measures, 39(2), 779.