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Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm

Author

Listed:
  • Qiuli Zheng

    (Xinjiang Agricultural University, Urumqi 830052, China)

  • Chunfang Yue

    (Xinjiang Agricultural University, Urumqi 830052, China)

  • Shengjiang Zhang

    (Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, China)

  • Chengbao Yao

    (Xinjiang Agricultural University, Urumqi 830052, China)

  • Qin Zhang

    (Xinjiang Agricultural University, Urumqi 830052, China)

Abstract

Xinjiang is located in the arid region of northwestern China, and agriculture accounts for an absolute share of total water use. Resource-based, engineering, structural, and managed water shortages coexist. Therefore, it is of great significance to vigorously develop water conservation technology and improve the efficiency of water transmission and distribution in canal systems. This research aims at addressing the problems of difficult manual regulation and the overall optimization of the final canal system, low-water-resource utilization efficiency, and management efficiency. Taking the branch-double two-stage canal system of Dongfeng branch canal in Mangxiang, Jinghe irrigation district, as a case study, and the rotation irrigation group and irrigation duration as decision variables, canal distribution is modeled with the goal of minimizing seepage losses. The improved grey wolf algorithm combined with particle swarm optimization is used for the first time and compared with the traditional grey wolf algorithm, genetic particle swarm optimization fusion algorithm, and northern goshawk algorithm. The results show that (1) on the basis of meeting the water discharge capacity and water demand requirements of the canal system, the diversion time of the water distribution scheme obtained by using the improved grey wolf algorithm is shortened from 11 d to 8.91 d compared with the traditional empirical water distribution scheme. (2) The improved grey wolf algorithm converges to the optimal value within 10 generations compared to the remaining methods, and the total water leakage is reduced from 16.15 × 10 4 m 3 to 11.75 × 10 4 m 3 . (3) The number of gate adjustments is reduced, and the canal gates are opened and closed at the same time within each rotational irrigation group. The grey wolf algorithm improved by its combination with particle swarm has stronger optimization ability and convergence, which can better meet the requirements of efficient water resource allocation in irrigation canal systems, as well as a high application value.

Suggested Citation

  • Qiuli Zheng & Chunfang Yue & Shengjiang Zhang & Chengbao Yao & Qin Zhang, 2024. "Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm," Sustainability, MDPI, vol. 16(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3635-:d:1383573
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    References listed on IDEAS

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    1. Zhang, Xiaoxing & Guo, Ping & Zhang, Fan & Liu, Xiao & Yue, Qiong & Wang, Youzhi, 2021. "Optimal irrigation water allocation in Hetao Irrigation District considering decision makers’ preference under uncertainties," Agricultural Water Management, Elsevier, vol. 246(C).
    2. Liao, Xiangcheng & Mahmoud, Ali & Hu, Tiesong & Wang, Jinglin, 2022. "A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss," Agricultural Water Management, Elsevier, vol. 274(C).
    3. Li, Shuoyang & Yang, Guiyu & Wang, Hao & Song, Xiufang & Chang, Cui & Du, Jie & Gao, Danyang, 2023. "A spatial-temporal optimal allocation method of irrigation water resources considering groundwater level," Agricultural Water Management, Elsevier, vol. 275(C).
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