Cai et al. (2025) Multi-Objective Optimization for Irrigation Canal Water Allocation and Intelligent Gate Control Under Water Supply Uncertainty

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-17
• Authors: Qianqian Cai, Xianghui Xu, Mo Li, Xinping Ye, Wuyuan Liu, Hongda Lian, Yanlai Zhou
• DOI: 10.3390/w17243585

Research Groups

Not explicitly stated in the provided text, likely water resources or hydraulic engineering departments.

Short Summary

This study proposes an integrated framework combining interval-based uncertainty analysis, intelligent optimization, and advanced control for open-channel irrigation systems, demonstrating significant improvements in water allocation efficiency and gate control performance under uncertain water supply conditions.

Objective

• To develop and apply an integrated framework for canal water allocation and gate control that addresses water supply uncertainty and insufficient gate control precision in open-channel irrigation systems.

Study Configuration

• Spatial Scale: Chahayang irrigation district (a specific irrigation system).
• Temporal Scale: Annual water supply, irrigation cycles (45 days to 40.54 days), and dynamic gate control (implying shorter operational scales for control).

Methodology and Data

• Models used: Auto-Regressive Integrated Moving Average (ARIMA) model, Maximum Likelihood Estimation (MLE), Bi-objective optimization model, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller, Particle Swarm Optimization (PSO) algorithm.
• Data sources: Historical inflow data (implied for ARIMA prediction and MLE for uncertainty quantification).

Main Results

• Total canal seepage decreased by 1.21 × 10^7 cubic metres (m³), accounting for 3.9% of the district’s annual water supply.
• The irrigation cycle was shortened from 45 days to 40.54 days, improving efficiency by 9.91%.
• Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduced overshoot by 4.84% and shortened regulation time by 39.51%.

Contributions

• Development of an integrated framework combining interval-based uncertainty analysis, bi-objective optimization for water allocation, and PSO-tuned Fuzzy PID for gate control in open-channel irrigation systems.
• Demonstrated significant quantitative improvements in irrigation water allocation efficiency (reduced seepage, shortened cycle) and dynamic gate control performance (reduced overshoot, faster regulation) under uncertain inflow conditions.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Cai2025MultiObjective,
  author = {Cai, Qianqian and Xu, Xianghui and Li, Mo and Ye, Xinping and Liu, Wuyuan and Lian, Hongda and Zhou, Yanlai},
  title = {Multi-Objective Optimization for Irrigation Canal Water Allocation and Intelligent Gate Control Under Water Supply Uncertainty},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17243585},
  url = {https://doi.org/10.3390/w17243585}
}