A New Optimal Water Level Evaluation Strategy of the Great Lakes

Abstract

With the change of environment and the change of social supply and demand, the current optimal water level evaluation methods for the Great Lakes in North America are outdated in many aspects. Therefore, proposing new optimal water level evaluation strategies is crucial for water level management and control of the Great Lakes. The determination of the optimal water level for the Great Lakes is obviously a complex optimization evaluation problem. Therefore, we have developed a multi-objective optimization model based on the idea of multi-objective nonlinear programming algorithm. We first use the AHP method to comprehensively analyze the various factors that affect the water level of the Great Lakes, and then substitute them into the Monte Carlo Simulations model to find the approximate range of optimal water level for each lake. Subsequently, taking this range as the initial value, we employed the multi-objective nonlinear programming algorithm to further solve the problem, ultimately obtaining the optimal water level situation for the Great Lakes that can balance the current social demand.