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Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming

Author

Listed:
  • Reza Sepahvand

    (Isfahan University of Technology)

  • Hamid R. Safavi

    (Isfahan University of Technology)

  • Farshad Rezaei

    (Isfahan University of Technology)

Abstract

In arid and semi-arid regions, climate change causes a drastic decline in the volume of water resources as water demands increase. Thus, the present study is aimed at using a simulation-optimization model to perform conjunctive management of surface-ground water use to achieve two main objectives: (1) minimizing shortages in meeting irrigation water demands and (2) maximizing the total agricultural net benefit for the main crops of an agricultural sector. To meet these main goals, first, the genetic programming (GP) method is used to simulate surface water-groundwater interactions. Then, the simulation model is linked to a multi-objective genetic algorithm (MOGA) as the optimization model, yielding a simulation-optimization model. In order to investigate the impact of different climatic conditions on the optimized surface and ground water allocation and propose an optimal crop pattern for each climatic period, three planning periods (wet, normal and dry) were addressed in modeling the conjunctive water use management problem. Finally, the economic results of this study suggested a maximum increase in the net benefit by 38.19%, 59.37% and 45%, as compared to those obtained in the actual operation in wet, normal and dry years, respectively, for one study sub-area. The net benefit was also increased by at most 84.79%, 83.3% and 120.77% in wet, normal and dry years, respectively, for another study sub-area, demonstrating the competence of the optimal conjunctive use model to enhance net benefits with the least negative socio-environmental impacts resulting from any development and management scheme in the field of water resources.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:6:d:10.1007_s11269-019-02229-4
    DOI: 10.1007/s11269-019-02229-4
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    References listed on IDEAS

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    1. Hamid Safavi & Fatemeh Darzi & Miguel Mariño, 2010. "Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 1965-1988, August.
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    5. Chao-Chung Yang & Liang-Cheng Chang & Chang-Shian Chen & Ming-Sheng Yeh, 2009. "Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 417-437, February.
    6. Singh, Ajay, 2014. "Simulation–optimization modeling for conjunctive water use management," Agricultural Water Management, Elsevier, vol. 141(C), pages 23-29.
    7. Li, Mo & Fu, Qiang & Singh, Vijay P. & Liu, Dong, 2018. "An interval multi-objective programming model for irrigation water allocation under uncertainty," Agricultural Water Management, Elsevier, vol. 196(C), pages 24-36.
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    Cited by:

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    2. 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.
    3. Jain, Sonal & Ramesh, Dharavath & Trivedi, Munesh C. & Edla, Damodar Reddy, 2023. "Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production," Agricultural Water Management, Elsevier, vol. 279(C).
    4. Ali Arefinia & Omid Bozorg-Haddad & Khaled Ahmadaali & Javad Bazrafshan & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8378-8396, June.
    5. 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).
    6. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.
    7. Aryan Salvati & Alireza Moghaddam Nia & Ali Salajegheh & Parham Moradi & Yazdan Batmani & Shahabeddin Najafi & Ataollah Shirzadi & Himan Shahabi & Akbar Sheikh-Akbari & Changhyun Jun & John J. Clague, 2023. "Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2657-2690, September.

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