Research on Optimal Water Level of Great Lakes Based on Multi-Objective Optimization Model and Pid Control Algorithm

This study addresses the problem of water level management in the Great Lakes of the United States and Canada by integrating the needs of multiple stakeholders and proposing a combination of a multi-objective optimization model and a PID control algorithm. The PID parameters are optimized by interpolation, hierarchical analysis and genetic algorithm to determine the optimal water level and achieve precise regulation. The 2017 Lake Ontario data is used as an example to verify the effectiveness of the algorithm, analyze the effect of precipitation on water level and evaluate the sensitivity of the algorithm. Linear regression and Pearson's correlation coefficient are utilized to explore the performance of the algorithm under rainy conditions, providing a feasible reference for water level management in the Great Lakes.