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

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.