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Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm

Author

Listed:
  • Ali Sardar Shahraki

    (Department of Agricultural Economics, University of Sistan and Baluchestan, Zahedan 98167-45845, Iran)

  • Mohim Tash

    (Department of Entrepreneurship, University of Sistan and Baluchestan, Zahedan 98167-45845, Iran)

  • Tommaso Caloiero

    (Research Institute for Geo-Hydrological Protection (CNR-IRPI), National Research Council of Italy, 87036 Rende, Italy)

  • Ommolbanin Bazrafshan

    (Department of Natural Resources, University of Hormozgan, Bandar Abbas 79161-93145, Iran)

Abstract

Water is an irreplaceable commodity with a high economic value. Today, water scarcity is the biggest challenge in the world, and the crises arising from lack of freshwater resources are serious threats to sustainable environmental development and human health and welfare. As the problems grow in complexity and dimensions, it becomes less possible to solve them with conventional optimization methods or explicit computational methods in a proper amount of time and with the currently limited computation memory, making it very difficult to achieve an optimal absolute solution. In this regard, metaheuristic algorithms that are generally inspired by nature are used in complex optimization problems. The Pishin Dam is an important dam in the eastern basin of Iran in the south of Sistan and Baluchestan province. This region faces severe water stress due to very low precipitation and very high evaporation on the one hand and the growing increase in urban, agricultural, and industrial demand on the other hand. The water development plans executed by the Ministry of Energy in the studied region influence water supply and demand profoundly. This research investigated the optimal allocation of water resources of this dam under management scenarios using the metaheuristic technique of the ant colony optimization algorithm (ACO). The results showed that the best value of the objective function was 82.3658 million m 3 . When applying the scenario of developing the cultivation area, the best value was obtained at 67.1258, which was significantly different from the base state. The results show that the ACO algorithm is suitable for the water resources of the Pishin Dam and can be used in planning and policymaking.

Suggested Citation

  • Ali Sardar Shahraki & Mohim Tash & Tommaso Caloiero & Ommolbanin Bazrafshan, 2024. "Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm," Sustainability, MDPI, vol. 16(13), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5801-:d:1431104
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    References listed on IDEAS

    as
    1. Ali Sardar Shahraki & Thomas Panagopoulos & Hajar Esna Ashari & Ommolbanin Bazrafshan, 2023. "Relationship between Indigenous Knowledge Development in Agriculture and the Sustainability of Water Resources," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    2. M. Babel & A. Gupta & D. Nayak, 2005. "A Model for Optimal Allocation of Water to Competing Demands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(6), pages 693-712, December.
    3. Giupponi, C & Mysiak, J & Fassio, A & Cogan, V, 2004. "MULINO-DSS: a computer tool for sustainable use of water resources at the catchment scale," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 13-24.
    4. Ali Sardar Shahraki & Tommaso Caloiero & Ommolbanin Bazrafshan, 2023. "Influence of Climatic Factors on Yields of Pistachio, Mango, and Bananas in Iran," Sustainability, MDPI, vol. 15(11), pages 1-14, June.
    5. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    6. Bazrafshan, Ommolbanin & Ehteram, Mohammad & Moshizi, Zahra Gerkaninezhad & Jamshidi, Sajad, 2022. "Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches," Agricultural Water Management, Elsevier, vol. 273(C).
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