Application of Back-Propagation Artificial Neural Network Models for Prediction of Groundwater Levels: Case study in Western Jilin Province, China

Evaluation and forecast of groundwater levels through specific model helps in forecasting of groundwater resources. Among the different robust tools available, the back-propagation artificial neural network (BPANN) model is commonly used to empirically forecast hydrological variables. Here, we discuss the modeling process and accuracy of this method based on the root mean squared error (RMSE), the mean absolute error (MAE) and coefficient of efficiency (R²). The arid and semi-arid areas of western Jilin province (China) were chosen as study area owing to the decline of groundwater levels during the past decade mainly due to over exploitation. The simulations results indicated that BPANN is accurate in reproducing (fitting) and forecasting the groundwater levels time series based on the R² are 0.97 and 0.74, respectively. The RMSE, MAE for BPANN model in the predicting stage are 0.08, 0.066, respectively. It is evident that the BPANN is able to predict the groundwater levels reasonable well.