Research on Great Lake Water Level Based on NSGA-II and MPC Control Models

NSGA-II is an algorithm widely used to solve multi-conflict target optimization problems, and the MPC control model is an optimization strategy framework for multi-factor control. In this paper, we combine the two to explore their application in multi-objective and multi-factor scenarios and conduct an empirical analysis on the water level of The Great Lake. Firstly, a multi-objective optimization function was established through NSGA-II to maximize the overall interests of The Great Lake water level stakeholders. Subsequently, the MPC water level control system was used to construct a water level control model considering multiple influencing factors, which made it possible to maximize the interests of the owners and avoid the risk of natural disasters within the control of the water level. The research will not only help improve the well-being of stakeholders, but also provide strong support for the response to global climate change and the realization of green development.