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Optimal Water Level Prediction and Control of Great Lakes Based on Multi-Objective Planning and Fuzzy Control Algorithm

Author

Listed:
  • Ruizhi Ouyang

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Yang Wang

    (School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China)

  • Qin Gao

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Xinlu Li

    (School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 100101, China)

  • Qihang Li

    (School of Mathematics and Information Science, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Kaiye Gao

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China
    Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100864, China
    Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

The optimal water level prediction and control of the Great Lakes is critical for balancing ecological, economic, and societal demands. This study proposes a multi-objective planning model integrated with a fuzzy control algorithm to address the conflicting interests of stakeholders and dynamic hydrological complexities. First, a network flow model is established to capture the interconnected flow dynamics among the five Great Lakes, incorporating lake volume equations derived from paraboloid-shaped bed assumptions. Multi-objective optimization aims to maximize hydropower flow while minimizing water level fluctuations, solved via a hybrid Ford–Fulkerson and simulated annealing approach. A fuzzy controller is designed to regulate dam gate openings based on water level deviations and seasonal variations, ensuring stability within ±0.6096 m of target levels. Simulations demonstrate rapid convergence (T = 5 time units) and robustness under environmental disturbances, with sensitivity analysis confirming effectiveness in stable conditions (parameter ≥ 0.2). The results highlight the framework’s capability to harmonize stakeholder needs and ecological sustainability, offering a scalable solution for large-scale hydrological systems.

Suggested Citation

  • Ruizhi Ouyang & Yang Wang & Qin Gao & Xinlu Li & Qihang Li & Kaiye Gao, 2025. "Optimal Water Level Prediction and Control of Great Lakes Based on Multi-Objective Planning and Fuzzy Control Algorithm," Sustainability, MDPI, vol. 17(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3690-:d:1637852
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    References listed on IDEAS

    as
    1. Elham Fijani & Khabat Khosravi, 2023. "Hybrid Iterative and Tree-Based Machine Learning Algorithms for Lake Water Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5431-5457, November.
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