Medium and long term runoff prediction method based on secondary decomposition and echo state network

Abstract

The invention discloses a medium and long term runoff prediction method based on secondary decomposition and an echo state network (ESN), and belongs to the technical field of runoff prediction. The medium and long term runoff prediction method comprises the following steps: 1, acquiring a runoff sequence x(t), and dividing the runoff sequence x(t) into a training sample and a test sample according to the data condition of the runoff sequence; 2, decomposing the runoff sequence into a plurality of intrinsic mode functions (IMF) and a trend term (Res) by using adaptive noise complete empiricalmode decomposition (CEEMDAN); 3, performing secondary decomposition on the IMF component with the highest frequency by using a variational mode decomposition (VMD) method to obtain a plurality of variational modes (Mode); and 4, respectively inputting the sub-sequences decomposed twice into the ESN for prediction, and reconstructing each prediction result to obtain a final prediction value. According to the medium and long term runoff prediction method, the problem that the prediction error of the high-frequency component in primary decomposition is large is solved, and the prediction precision of the ESN is further improved.

Description

technical field [0001] The invention relates to a medium and long-term runoff prediction method based on secondary decomposition and echo state network (ESN), belonging to the technical field of runoff prediction. Background technique [0002] At present, the most widely used method of runoff prediction is the artificial intelligence method mainly represented by artificial neural network, but the traditional recurrent neural network has the disadvantage of memory fading. Therefore, ESN with good short-term memory function, simple training algorithm and easy determination of network structure has attracted more and more researchers' attention. However, the complexity of the runoff sequence affects the parameter calibration and learning efficiency of the ESN model. A single model cannot fully mine and learn all the information contained in the runoff sequence, and the room for improving the prediction accuracy is limited. The combined forecasting method of the "structure" mod...

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to provide a medium and long-term runoff prediction method based on secondary decomposition and echo state network (ESN), which solves the problem that the highest frequency component still fluctuates strongly in the primary decomposition, and further improves the prediction accuracy of the model with better performance , which has a good reference value for medium and long-term runoff forecasting

Images