A new groundwater depth prediction model based on EMD-LSTM

Abstract

Abstract Groundwater resources play an important role in life, but the lack of effective management of groundwater resources has led to a regional decline in groundwater levels. How to curb the over-exploitation of groundwater and realize the sustainable use of groundwater resources has become an important issue for ecological environmental protection. Therefore, accurate prediction of groundwater depth is an important foundation for the rational use of groundwater resources. Based on the significant advantages of empirical model decomposition (EMD) in dealing with non-smooth and nonlinear data and the long-term memory function of long and short-term memory (LSTM) network, a coupled EMD-LSTM-based groundwater prediction model is constructed and apply to groundwater depth prediction of three professional observation wells. The results show that the maximum relative error of the prediction results of the EMD-LSTM model for the three professional observation wells is 5.00% and the minimum is 0.07%. The prediction passing rate is 100% and the prediction accuracy of the coupled model for groundwater depth is higher than that of the single LSTM model and back-propagation (BP) model. In conclusion, the model has high prediction accuracy and provides an effective method for the prediction of groundwater depth. HIGHLIGHTS Empirical mode decomposition (EMD) is a relatively novel data preprocessing method that can effectively reduce the non-smoothness of time series. LSTM is considered to be superior in terms of learning rate and generalization ability. The EMD-LSTM coupled model has better nonlinear and complex process learning ability in groundwater forecasting.