Developing Fuzzy Optimization Model for Conjunctive Use of Surface and Ground Water, Case Study: Astaneh-Koch Esfahan Plain

Document Type : Research Article

Authors

1 MSc of Water Resources Engineering, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran.

2 Assistant Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran

3 Associate Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran

Abstract

In this study, an entirely fuzzy optimization model is presented for conjunction use of surface and groundwater. Groundwater level in Astaneh-Koch Esfahan Aquifer was simulated using the GMS Model, while its results were used as a constraint in optimization model. Then, Kumar and Jayalakishimi fuzzy optimization methods were solved by applying the GAMS software. Maximum water supply shortage in Kumar method for left-side of Sangar was in 2009 that 58.36% of demands was satisfied. Also this value in the right-side was calculated about 56.76% in 2008. In the Jayalakishimi method, the maximum water supply shortage was obtained in 1998 and 2014 that 66.5% and 60.96% of demands for left and right-side are satisfied, respectively. On the other hand, for this method, in the situation of total maximum shortage for left and right sides of Sangar channel, supply percentage of water needs was about 65.9% in 1998, while for the Jayalakishimi method, it was obtained about 66.5% in 1998. Also in the current situation, the supply percentage in the worst conditions is 54%. Regarding consideration of uncertainties, the proposed fuzzy optimization model can be applied to manage the conjunctive water supply for agriculture.

Keywords

Main Subjects


[1]. Todd KD, Mays LW. Groundwater hydrology. John Wiley & Sons, Inc, NJ. 2005.
[2]. Buras N. Conjunctive operation of dams and aquifers. Journal of the Hydraulics Division. 1963; 89(6):111-31.
[3]. Abadi A, Kholghi A, Bozorg Hadad O, Mohammadi K. Providing operational rules for real-time management of surface water and underground water resources. MSc Thesis.2010. University of Tehran, Pardis-Karaj. [Persian]
 
[4]. Bazargan-Lari, MR, Kerachian R, Mansoori A. A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study. Environmental management. 2009; 43(3):470-82.
[5]. Karamouz M, Tabari. MMR, Kerachian R. Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International. 2007; 32(1):163–176.
[6]. Mohammad Rezapour Tabari M, Ebadi T, Maknon R. Development of a Smart Model for Groundwater Level Prediction Based on Aquifer Dynamic Conditions. Water and Wastewater Journal. 2010; Volume: 21(4): 70-80. [Persian]
[7]. Azari, A, Radmanesh F. Simulation-Multi-Purpose Optimization for Integrated Water Resources Management in Surface Water and Underground Water Interactions Using Genetic Algorithm (Case Study: Dasht Daz), MSc Thesis.2013; Shahid Chamran University of Ahvaz. [Persian]
[8]. Safavi H.R, Alijanian M.A. Optimal Crop Planning and Conjunctive Use of Surface Water and Groundwater Resources Using Fuzzy Dynamic Programming. Journal of irrigation and drainage engineering © ASCE. 2011; 383-397.
[9]. Safavi HR, Chakraei I, Kabiri-Samani A, Golmohammadi MH. Optimal reservoir operation based on conjunctive use of surface water and groundwater using neuro-fuzzy systems. Water resources management. 2013; 27(12):4259-75.
[10]. Safavi HR, Enteshari S. Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management. 2016; 173:23-34.
[11]. Tabari MMR. Conjunctive Use Management under Uncertainty Conditions in Aquifer Parameters. Water Resources Management. 2015; 29(8):2967-86.
[12]. Chang L-C, Chu H-J, Chen Y-W. A fuzzy inference system for the conjunctive use of surface and subsurface water. Advances in Fuzzy Systems. 2013; 2013:2.
[13]. Rezaei F, Safavi HR, Mirchi A, Madani K. F-MOPSO: an alternative multi-objective PSO algorithm for conjunctive water use management. Journal of Hydro-environment Research. 2017; 14:1-18.
[14]. Safavi H, Rezaei F. Conjunctive use of surface and ground water using fuzzy neural network and genetic algorithm. Iranian Journal of Science and Technology Transactions of Civil Engineering. 2015; 39(C2):365.
[15]. Rezaei F, Safavi HR, Zekri M. A hybrid fuzzy-based multi-objective PSO algorithm for conjunctive water use and optimal multi-crop pattern planning. Water Resources Management. 2017; 31(4):1139-55.
[16]. Langeroudi M.R, Kerachian R. .Developing Operating Rules for Conjunctive Use of Surface and Groundwater Considering the Water Quality Issues. KSCE Journal of Civil Engineering. 2014; 18(2):454-461.
[17]. Chen, Y. W., Chang, L. C., Huang, C. W. and Chu, H. J. Applying genetic algorithm and neural network to the conjunctive use of surface and subsurface water. Water resources management.2013; 27: 4731-4757.
[18]. Sahoo, B., Lohani, A. K. and Sahu, R. K. Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water resources management. 2006;20: 931-948.
[19]. Li, M., Fu, Q., Singh, V. P., Ma, M. and Liu, X. An intuitionistic fuzzy multi-objective non-linear programming model for sustainable irrigation water allocation under the combination of dry and wet conditions. Journalof Hydrology. 2017;
[20]. Asaadi Mehrabani, M. Banihabib, M. Roozbahany A. Fuzzy Linear Programming Model for the Optimization of Cropping Pattern in Zarrinehroud Basin. Iran Water Resources Research. 2018; 14(1), 13-24. [Persian]
[21]. Continuation of the study of the plains with a quantitative and qualitative quantitative and qualitative study network of Astaneh-Kochi Esfahan 1389-1390, [Persian]
[22]. Pandam Consulting Engineers. Improvement of irrigation and drainage network in Gilan aquifer. 1383; Volume Four. [Persian]
[23]. Harbaugh AW, Banta ER, Hill MC, McDonald MG. MODFLOW-2000, the U.S. Geological Survey modular groundwater model.2000; Report No. 00–92, U.S. Geological Survey, Denver.
[24]. Klir GJ, Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh: World Scientific Publishing Co., Inc.; 1996.
[25]. Zadeh, L.A. Fuzzy sets. J. of Information and Control, 1965; 8(3), 338-353.
[26]. Kumar A, Kaur J, Singh P. Fuzzy optimal solution of fully fuzzy linear programming problems with inequality constraints. 2010.
[27]. Kumar A, Kaur J, Singh P. A new method for solving fully fuzzy linear programming
problems. Applied Mathematical Modelling. 2011; 35(2):817-23.
[28]. Jayalakshmi M, Pandian P. A new method for finding an optimal fuzzy solution for fully fuzzy linear programming problems. International Journal of Engineering Research and Applications. 2012; 2(4):247-54.