Dynamic Network Modeling of Optimal Water Level Control Based on RMOBM and DMOWN

In this paper, the optimal water level of the Great Lakes and their connected waterways in North America is studied by using the multi-objective bargaining model based on water level risk (RMOBM model) and the dynamic multi-objective basin network (DMOWN model). First of all, we established the RMOBM model, taking into account the influence of rainfall, snowfall, evaporation of lake water and surface leakage on water level, and carried out multiple rounds of bargaining with the objective function of maximizing economic benefits and minimizing the probability of flood occurrence, and obtained the optimal water level results by using MATLAB software. Secondly, based on the RMOBM model and the maximum flow target value, we considered the flow regulation effect of Moses-Sanders Dam project and Cornwall County in the DMOWN model, obtained the maximum flow value through the Ford-Fulkerson algorithm, and then output the optimal lateral flow results of five major water level nodes. These studies provide a scientific basis for the management of North America's Great Lakes and their associated waterways and help develop effective planning and management strategies.