Groundwater Depth Prediction Based on Wavelet Decomposition-LSTM Network

Abstract

The time series of groundwater depth has the characteristics of trend, abrupt, and non-stationary. Based on the advantages of wavelet decomposition and long short-term memory neural network (LSTM), a new coupling model (wavelet decomposition-LSTM) for groundwater depth prediction is proposed. Firstly, wavelet decomposition is applied to decompose the groundwater data into high-frequency periodic signals and low-frequency trend signals, to reduce the complexity of the time series. Secondly, the decomposed high-frequency and low-frequency signals are taken as inputs to train the model respectively, and then the total prediction value is acquired. The improved model reduces the limitations of LSTM processing complex signals and improves prediction accuracy. Taking the No. 5 well of Lu Wangfen Town and the No. 3 well of Muye Town as the research object, the achieved results from the proposed model were compared with the results of the LSTM model and the back-propagation (BP) neural network model. This comparison shows that the performance of the new model is better than the others, and the average relative errors of the coupling model are 2.11% and 2.49% respectively. The proposed method has high prediction accuracy and generalization ability and is a more effective method for groundwater depth prediction.