The optimal water level control method of cascaded lakes based on particle swarm optimization algorithm

As the basic conditions of agriculture, fishery and inland navigation, inland lakes play an irreplaceable and important role in human production and life. But with the continuous deterioration of ecological environment in recent years, the contradiction between river water supply and human needs is becoming increasingly prominent. In order to solve the problem of spatiotemporal imbalance of water supply, an optimal water level control model based on particle swarm optimization algorithm was proposed. Firstly, the water level demand weights of different stakeholders were determined through historical data, and the optimal water levels of each lake at different times were obtained by weighting under the influence of natural factors. Through the regulation of DAMS at the junction of cascaded lakes, t he water flow can be reasonably controlled to meet the needs of life and production to the maximum extent within the adjustable range. Taking the water level control of the Great Lakes in North America as an example, the effectiveness of the algorithm is verified based on historical data. The results show that the algorithm can effectively meet the needs of production and life, and reduce the risk of natural disasters by 74.9%.