Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China

In this study, the ability of a wavelet analysis–artificial neural network (WA-ANN) conjunction model for multi-scale monthly groundwater level forecasting was evaluated in an arid inland river basin, northwestern China. The WA-ANN models were obtained by combining discrete wavelet transformation with ANN. For WA-ANN model, three different input combinations were trialed in order to optimize the model performance: (1) ancient groundwater level only, (2) ancient climatic data, and (3) ancient groundwater level combined with climatic data to forecast the groundwater level for two wells in Zhangye basin. Based on the key statistical measures, the performance of the WA-ANN model was significantly better than ANN model. However, WA-ANN model with ancient groundwater level as its input yielded the best performance for 1-month groundwater forecasts. For 2- and 3-monthly forecasts, the performance of the WA-ANN model with integrated ancient groundwater level and climatic data as inputs was the most superior. Notwithstanding this, the WA-ANN model with only ancient climatic data as its inputs also exhibited accurate results for 1-, 2-, and 3-month groundwater forecasting. It is ascertained that the WA-ANN model is a useful tool for simulation of multi-scale groundwater forecasting in the current study region.