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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zahra Kayhomayoon & Sami Ghordoyee Milan & Naser Arya Azar & Pete Bettinger & Faezeh Babaian & Abolfazl Jaafari, 2022. "A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2691-:d:758451
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    References listed on IDEAS

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    1. Farshad Rezaei & Hamid R. Safavi, 2022. "Sustainable Conjunctive Water Use Modeling Using Dual Fitness Particle Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 989-1006, February.
    2. Pan, Dan & Chen, Huan, 2021. "Border pollution reduction in China: The role of livestock environmental regulations," China Economic Review, Elsevier, vol. 69(C).
    3. Yu Chen & Liang Chang & Chun Huang & Hone Chu, 2013. "Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4731-4757, November.
    4. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Reza Sepahvand & Hamid R. Safavi & Farshad Rezaei, 2019. "Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2123-2137, April.
    6. Yousefi, Maryam & Banihabib, Mohammad Ebrahim & Soltani, Jaber & Roozbahani, Abbas, 2018. "Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater," Agricultural Water Management, Elsevier, vol. 208(C), pages 224-231.
    7. Mehrabi, Ahmad & Heidarpour, Manouchehr & Safavi, Hamid R. & Rezaei, Farshad, 2021. "Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm," Agricultural Water Management, Elsevier, vol. 246(C).
    8. Vedula, S. & Mujumdar, P.P. & Chandra Sekhar, G., 2005. "Conjunctive use modeling for multicrop irrigation," Agricultural Water Management, Elsevier, vol. 73(3), pages 193-221, May.
    9. Hamid Safavi & Mahdieh Esmikhani, 2013. "Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2623-2644, May.
    10. Safavi, Hamid R. & Enteshari, Sajad, 2016. "Conjunctive use of surface and ground water resources using the ant system optimization," Agricultural Water Management, Elsevier, vol. 173(C), pages 23-34.
    11. D.-A. An-Vo & S. Mushtaq & T. Nguyen-Ky & J. Bundschuh & T. Tran-Cong & T. Maraseni & K. Reardon-Smith, 2015. "Nonlinear Optimisation Using Production Functions to Estimate Economic Benefit of Conjunctive Water Use for Multicrop Production," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2153-2170, May.
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    Cited by:

    1. Saeid Akbarifard & Mohamad Reza Madadi & Mohammad Zounemat-Kermani, 2024. "An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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