Optimization of water level management in the Great Lakes based on simulated annealing and Kalman filtering

This paper focuses on the water level management problems in the Great Lakes, and addresses the challenges of optimal water level regulation in the Great Lakes through methods such as simulated annealing multi-objective optimization algorithm and Kalman filter hydrological model. First, the simulated annealing algorithm was used to analyze the influence of each interest factor on the water level, and the interest balance point was established as the optimization objective function to obtain the optimal water level in the Great Lakes. Second, a Kalman filter hydrological model was introduced to keep the water level of the lakes fluctuating around the optimal level by monitoring and adjusting the inflow and outflow data of the lakes in real time to adapt to the effects of climate change and anthropogenic factors. Finally, a hierarchical analysis was applied to assess the importance of each stakeholder for water level management and to verify the robustness and reliability of the algorithm in case of conflicting interests. This study can provide scientific decision support for water resource management in the Great Lakes region.