Research on the Application of RBF Based on Combined Models in Hydrological Flow Forecasting
In view of the poor accuracy of hydrological flow forecast at present,a combined model based on radial basis function(RBF)neural network and long short term memory(LSTM)neural network is proposed to improve the accuracy of forecast.And a real-time correction method for traffic prediction was proposed based on the least squares method.In the experimental results,compared to RBF and LSTM single models,the error of RBF-LSTM in predicting traffic after 30 days decreased by 60.87%and 65.27%,respectively;compared to before correction,the RBF-LSTM prediction error after correction decreased by 48.15%,verifying the effectiveness of the proposed method in the study.The research provides reference for improving the accuracy of hydrological flow forecasting and flood prevention and control work.
NETL
NSTL
万方数据
维普
