Research on optimal water level algorithm based on The Great Lakes

In this paper, we establish multi-objective optimization model and water level prediction model through multi-task learning algorithm, entropy weight analytic hierarchy process (AHP), convolutional neural network (CNN) and Long Short-Term Memory (LSTM) neural network, etc., and calculated weights of 0.665, 0.761, 0.866, and 0.122 for water level elevation, river flow, cyclic variation, and water level stabilization, respectively, and Lake Ontario’s optimal water level of 74.926 m.