Redesign of Groundwater Monitoring Network Kuhdasht Aquifer

Document Type : Research Article

Authors

1 Associate Professor, Faculty of New Sciences and Technologies, University of Tehran, Iran

2 Msc Student in Ecohydrology, University of Tehran, Iran

Abstract

Long-term groundwater monitoring networks can provide essential information for planning and management water resources. Budget constraints in water resource management agencies often mean a reduction in the number of observation wells included in a monitoring network. Due to economic considerations and reducing monitoring costs, the optimization approach in this study is to reduce the monitoring stations. In the present study, genetic algorithm was used to designing optimal water level monitoring network. The aim of optimization to determine an optimal combination (reduced) from the main network was well observed, so that the possibility of measurement error smallest and least loss of data and provide the best distribution of wells. Using the genetic algorithm, the groundwater monitoring network of the region was optimized. Using the genetic algorithm, the groundwater monitoring network of the region was optimized. In Koohdasht Plain, among of  15 observation wells, 13 wells observations had data. in order to do this research, the data for 36 consecutive months, namely, data from 1392 to 1394 were used. Using the genetic algorithm, the groundwater monitoring network was redesigned and selected wells were selected from potential points. finally, 28 wells had the lowest RMSE of  0.113 and had the best distribution. Location of wells obtained by a number of existing wells was near. The results obtained with the criteria of the quantity of groundwater monitoring network design, which corresponded to show the effectiveness of this approach.

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Main Subjects


[1]. Akbarzadeh M, Ghahraman B, Davari K. Optimization of Groundwater Quality Monitoring Network of Mashhad Aquifer Using Spatial-Temporal Modeling. Iran Water Resources Research. 2016;12(1), 144-133. [Persian]
[2]. Forughi F, Rezaei M. Optimization of Groundwater Monitoring Network in Tabriz Plain using Geostatistics Method. Quarterly Journal of Environmental Geology. 2012;7(22), 93-103. [Persian]
[3]. Ahmadvand M. Optimal design of the long-term groundwater monitoring system in the Hamedan-Bahar plain using an ant colony and a robust optimization. Master's Thesis, Islamic Azad University, Tehran-North Branch, Faculty of Science. 2013; 1-17. [Persian]
‏[4]. Nakhai M, Amiri V, Ahadi Dolatsara A. Optimization of groundwater monitoring network using ant colony algorithm. 2015; Vol 9, No.4, 174-171. [Persian]
[5]. Hushangi N, Ale sheikh A, Nadiri A. Optimization of the number of piezometers in predicting groundwater level using PCA and geostatistical methods. Journal of Water and Soil Science. 2015; Vol 25, No. 4.2, 66-53. [Persian]
[6]. Ganji Khoram del N, Keikhaei F. Optimal design of observation wells in an groundwater level monitoring network using an genetic algorithm. Watershed management research. 2015; 7(14), 159-166. [Persian]
[7]. Jason C. Fisher, Optimization of Water-Level Monitoring Networks in the Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method. Prepared in cooperation with the Bureau of Reclamation and U.S. Department of Energy, 2013.
[8]. Dhar, A. and Patil, R.S., “Multiobjective design of groundwater monitoring network under epistemic uncertainty”, Water Resources Management,2012; 26(7), 1809- 1825.
[9]. Ketabchi H, Ataie-Ashtiani B. Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges. Journal of Hydrology, 2015; 193–213.
[10]. Thakur J. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches. Hydrology. 2015; 2, 148-175.
[11]. Q. Luo, J. Wu, Y. Yang, J. Qian and J. Wu, “Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty. Journal of Hydrology. 2016; 534,, 352–363.
 
[12]. Deepti P, Kyna B, Vance C. Karthikeyan R, Optimization of a Water Quality Using a Spatially Referenced Water Quality Model and a Genetic Algorithm Monitoring Network” Journal of Water, 2017; 3-11.
[13]. Kumar Singh K, Bhaskar Katpata Y. Optimization of Groundwater Level Monitoring Network Using GIS-based Geostatistical Method and Multi-parameter Analysis: A Case Study in Wainganga Sub-basin, India, Chin. Geogra. Sci. 2017; 27 (2), 201–215.
[14]. Bashi-Azghadi, N. and Kerachian, R., Locating monitoring wells in groundwater systems using embedded optimization and simulation models, Science of the Total Environment, 2010; 408(10), 2189-2198
[15]. Pudineh O, Daliri M. Compare some geostatistical interpolation methods and meet certain depth to groundwater (Case study: Iranshahr-Bampour plain), Journal of Water Resources Engineering. 2017; 10, 82-100.
[16]. Mirzaei Nadushan F, Bozorg Hadad A, Khayat Kholghi M. Two Objective design of groundwater level monitoring network using NSGA-II in Eshtehard Plain. Iran Water Researches. 2016; 74(2), 345-354. [Persian]
[17]. Cressie, N.A.C., Statistics for spatial data, John Wiley & Sons; 1991.
[18]. AminZadeh Ghoharrizi B, Tohidi rad S, Asadi R. Application of NSGA-II algorithm for solving multi-objective location problems. Quarterly Journal of Urban Studies. 2016; 19, 15-25. [Persian]
[19]. Karami M, Habibi S, Zhaleh B. Nanocomposite Electromagnetic Absorber Design with Localized Particle Swarm Optimization and Genetic Algorithm Optimization Method. Magazine Research Systems. Nanosciences and Metamaterials from Simulation to Industry. 2015; 93-105. [Persian]