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Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Author

Listed:
  • Mahdieh Kalhori

    (University of Qom)

  • Parisa-Sadat Ashofteh

    (University of Qom)

  • Seyedeh Hadis Moghadam

    (University of Qom)

Abstract

This research uses the simulation–optimization approach for the conjunctive use of surface and underground water in climate change conditions. For this purpose, Multi-Objective Invasive Weed Optimization Algorithm (MOIWOA) is developed for three benchmark functions in order to verify the algorithm and its results in the form of Pareto Front with MOPSO and NSGA-II were compared. Then, developed MOIWOA is applied for optimal water allocation to drinking-industry and agricultural needs in order to (1) maximize reliability index of water supply system, and (2) maximize resiliency index of a water supply system(from a failure period due to lack of water supply). The findings show that the model effectively reduces failure periods and allocates water resources efficiently to consumption sectors during hot months. The highest increase in reliability and resiliency indexes is observed in the RCP85 climate change scenario (as pessimistic scenario) for time period 2070–2099, with an 11% increase in reliability and a 66% increase in resiliency. In this research, Gray Relationship Analysis (GRA) is used to select the best solution from the set of Pareto solutions. Also, multi-objective solutions are prioritized and ranked based on Gray Relational Grade (GRG).

Suggested Citation

  • Mahdieh Kalhori & Parisa-Sadat Ashofteh & Seyedeh Hadis Moghadam, 2023. "Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4433-4458, September.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:11:d:10.1007_s11269-023-03564-3
    DOI: 10.1007/s11269-023-03564-3
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    References listed on IDEAS

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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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