Application and comparison of two prediction models for groundwater levels: A case study in Western Jilin Province, China

Abstract

Evaluation and prediction of groundwater levels through specific model(s) helps in forecasting of groundwater resources. Among the different robust tools available, the Integrated Time Series (ITS) and Back-Propagation Artificial Neural Network (BPANN) models are commonly used to empirically forecast hydrological variables. Here, we discuss the modeling process and accuracy of these two methods in assessing their relative advantages and disadvantages based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and coefficient of efficiency (CE). The arid and semi-arid areas of western Jilin province of China were chosen as study area owing to the decline of groundwater levels during the past decade mainly due to overexploitation. The simulation results indicated that both ITS and BPANN are accurate in reproducing (fitting) the groundwater levels and the CE are 0.98 and 0.97, respectively. In the validation phase, the comparison of the prediction accuracy of the BPANN and ITS models indicated that the BPANN models is superior to the ITS in forecasting the groundwater levels time series in term of the RMSE, MAE and CE.