文献类型: 外文期刊
作者: Guilin Tian;Mouchao Lv;Ming Wang;Jingtao Qin;Yingying Wang;Jiankui Yu;Bo Yang
作者机构:
关键词: Beetle swarm optimization algorithm;Canal water distribution model;Canals-gates coupling;Irrigation scheduling;Theoretical flow rate
期刊名称: Computers and Electronics in Agriculture
ISSN: 0168-1699
年卷期: 2025 年 236 卷
页码:
收录情况: SCIE(2025版) ; ; EI(2025版)
摘要: Accurate and efficient canal water scheduling in irrigation districts is crucial for promoting agricultural water-saving policies, especially in the context of global climate instability. However, many studies neglected the role of gates in scheduling, and there were serious water distribution fairness problems in the actual irrigation district management of the farmland upstream and downstream. In this research, a novel method was proposed by integrating the traditional planning model (Dynamic Programming) with the canal water distribution framework. The former facilitated the effective coordination of gates and canals at multi-levels, while the latter addressed water requirements under various optimization objectives. A total of 32 scheduling schemes were obtained by applying the model to Dagong irrigation district, and the novel Beetle Swarm Optimization algorithm (BSO) and the mature Particle Swarm Optimization algorithm (PSO) were used to solve the problem, respectively. The new model also obtained the following results while solving the problems. (1) The main canal's Theoretical Flow Rate (TFR) is the critical factor influencing both the time efficiency and water utilization of the water distribution schemes. (2) A generalizable scheduling scheme is developed based on the observed flow and time distribution patterns in the main and sub-main canals, though its accuracy remains limited. (3) The comparison between the computational results of the BSO and PSO verifies the applicability of the former in this field. In conclusion, the model proposed enhances the efficiency and availability of the irrigation water delivery, applicable to other irrigation districts facing similar water allocation challenges.
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