Research on Model for Predicting Reservoir Water Level in Basin Based on Deep Learning and Extracting Spatiotemporal Information
In order to solve the problem of the influence factors of the connected reservoir water level being complex and non-stationary,which is difficult to predict directly through hydrological calculation,the daily rainfall sequence of the hydrological station in a small basin was analyzed.Firstly,the time series was denoised by wavelet transform.On this basis,the maximal information coefficient(MIC)correlation analysis was used to screen the correlation of the daily water level series,which increased the information density related to the input rainfall series and predicted water level.A pre-diction model of the LSTM,which introduces strong correlation sequence input into attention mechanism,was proposed to improve the ability of LSTM neural network to select and extract sequence features.Taking the measured daily rainfall sequence of a small watershed in Fujian Province as an example,the results show that the prediction error of this method is only 0.190 8.Compared with the LSTM model,this method effectively improves the prediction accuracy,which pro-vides a decision basis for the operation of reservoir water situation and flood control and disaster reduction management.
reservoir water level predictioncorrelation analysiswavelet transformattention mechanismLSTM
NETL
NSTL
万方数据
维普
