A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran

Zahra Kayhomayoon, Sami Ghordoyee Milan, Naser Arya Azar, Pete Bettinger, Faezeh Babaian and Abolfazl Jaafari
Additional contact information
Zahra Kayhomayoon: Department of Geology, Payame Noor University (PNU), Tehran 193954697, Iran
Sami Ghordoyee Milan: Department of Water Engineering, Aburaihan Campus, University of Tehran, Tehran 3391653755, Iran
Naser Arya Azar: Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
Pete Bettinger: Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
Faezeh Babaian: Department of Water Science and Engineering, Islamic Azad University, Science and Research Branch, Tehran 1477893855, Iran
Abolfazl Jaafari: Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran 1496813111, Iran

Sustainability, 2022, vol. 14, issue 5, 1-20

Abstract: Agricultural months are the critical period for the allocation of surface water and groundwater resources due to the increased demands on water supplies and decreased recharge rate. This situation urges the necessity of using conjunctive water management to fulfill the entire water demand. Here, we proposed an approach for aquifer stabilization and meeting the maximum water demand based on the available surface and groundwater resources and their limitations. In this approach, we first used the MODFLOW model to simulate the groundwater level to control the optimal withdrawal and the resulting drop. We next used a whale optimization algorithm (WOA) to develop an optimized model for the planning of conjunctive use to minimize the monthly water shortage. In the final step, we incorporated the results of the optimized conjunctive model and the available field data into the least squares-support vector machine (LS-SVM) model to predict the amounts of water shortage for each month, particularly for the agricultural months. The results showed that during the period from 2005 to 2020, the most water shortage belonged to 2018, in which only about 52% of water demand was met with the contribution of groundwater (67%) and surface water (33%). However, the groundwater level could have increased by about 0.7 m during the study period by implementing the optimized model. The results of the third part revealed that LS-SVM could predict the water shortage with better performance with a root-mean-square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe Index of 5.70 m, 3.43%, and 0.89 m, respectively. The findings of this study will enable managers to predict the water shortage in future periods to make more informed decisions for water resource allocation.

Keywords: water management; conjunctive use; water supply; optimization; WOA; LS-SVM; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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