Comparative Study on Mid-Long Term Prediction of Reservoir Inflow based on LSTM and BP Neural Network
In order to explore the application effect of different data-driven models in mid-long term runoff prediction,taking the inflow runoff of Lianghekou Reservoir and Jinping First Class Reservoir in the Yalong River Basin as the research objects,the long short-term memory neural network(LSTM)and BP neural network models were used to predict the annual and monthly run-off of each reservoir.A set of medium and long term runoff prediction factors was constructed based on previous runoff informa-tion and circulation impact factor data.The parameters of LSTM and BP neural network were optimized,and annual and monthly runoff prediction models for each reservoir were established.The prediction results show that the LSTM has higher accuracy in predicting annual and monthly runoff than the BP neural network model,and both models have higher accuracy in predicting run-off in the Lianghekou Reservoir than in the Jinping First Class Reservoir.The results can provide references for mid-long term runoff prediction of large hydropower stations.
LSTMBP Neural Networkmid-long term streamflow predictionYalong River Basin
万方数据
维普
