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Modeling GA-derived optimization analysis for canal-based irrigation water allocation under variations in runoff-related and irrigation-related factors

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  • Wu, Shiang-Jen
  • Yang, Han-Yuan
  • Chang, Che-Hao
  • Hsu, Chih-Tsung

Abstract

This study proposes an optimal analysis model for carrying out the canal-based irrigation water allocation (OPA_IWA_Canal) for a schedule-based irrigation zone under consideration of the variations in the runoff-related factor and irrigation-related factors. Coupled with the modified genetic algorithm based on the sensitivity of the model parameters (GA-SA) with a nonlinear objective function relying on the branch-based supplying satisfaction index, the proposed OPA_IWA_Canal model could optimally allocate the irrigation water supply under a desired demand. The Zhudong Canal irrigation zone with 15 branches is selected as the study area with the upstream inflow from the Shanping weir and two intake-water hydraulic structures (Baoshan Reservoir and Yuandon water treatment plant). The results from the model application on a variety of upstream inflow and water demands indicate that the proposed OPA_IWA_Canal model can provide the branch-based irrigation water supplies and water intake of the hydraulic structures to enhance the irrigation efficiency significantly, on average, from 0.25 to 0.7 under the insufficient upstream inflow. Additionally, by proceeding with the proposed OPA_IWA_Canal model under the various combinations of the unirrigated branches, all reaming branches could be availably allocated irrigation water supplies with a high irrigation efficiency (nearly 0.8).

Suggested Citation

  • Wu, Shiang-Jen & Yang, Han-Yuan & Chang, Che-Hao & Hsu, Chih-Tsung, 2023. "Modeling GA-derived optimization analysis for canal-based irrigation water allocation under variations in runoff-related and irrigation-related factors," Agricultural Water Management, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:agiwat:v:290:y:2023:i:c:s0378377423004535
    DOI: 10.1016/j.agwat.2023.108588
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