The Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO

The water level in front of the check gate of water transfer projects is affected by physical factors such as rainfall, terrain and hydraulic structures. Its fluctuation trend has strong non-linear and stochastic characteristics, and it is difficult to predict accurately and efficiently by hydrodynamic model. To solve the problem of predicting water level in front of check gate, a long short term memory (LSTM) neural network based on adaptive inertia weight comprehensive learning particle swarm optimization algorithm (AIW-CLPSO) is proposed. The AIW and CLPSO are adopted to improve the global optimization ability and convergence velocity of PSO in the proposed model. The model was applied to the water level prediction in front of the Chaohu Lake check gate. The example of the water level prediction in front of the Chaohu Lake check gate shows that the proposed model can obtain the optimal parameters of LSTM neural network, which overcomes the limitations of difficult parameter selection and inaccurate prediction.