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

Ruizhi Ouyang, Yang Wang (), Qin Gao, Xinlu Li, Qihang Li and Kaiye Gao ()
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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

Sustainability, 2025, vol. 17, issue 8, 1-19

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.

Keywords: Great Lakes; water level prediction; fuzzy control; multi-objective optimization; hydrological modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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