Forecasting groundwater level of karst aquifer in a large mining area using partial mutual information and NARX hybrid model.

Predicting the groundwater level of karst aquifers in North China Coalfield is essential for early warning of mine water hazards and regional water resources management. However, the dynamic changes of strata structure and hydrogeological parameters driven by coal mining activity cause challenges to the process-oriented groundwater model. In order to achieve accurate prediction of groundwater level in large mining areas, this study was the first to use the data-driven Nonlinear Autoregressive with External Input (NARX) model to predict the groundwater level of six karst aquifer observation wells in Pingshuo Mining Area. Three variable input scenarios were set up, solely considering meteorological factors, anthropogenic disturbance factors, and considering both meteorological and anthropogenic disturbance factors. The novel partial mutual information (PMI) screening algorithm was adopted to determine optimized input variables in each scenario. The input and feedback delay coefficients of NARX model were determined by using Seasonal-trend Decomposition Procedure Based on Loess (STL) algorithm and auto- and cross-correlation functions. The results showed that PMI algorithm can effectively screen out the optimal input variables for predicting groundwater level, the NSE coefficients of the PMI-NARX models under the three scenarios were 38.81%, 4.26% and 41.46% higher than those of the corresponding control experiments, respectively. In addition, the prediction performance of the PMI-NARX built on the basis of meteorological factors is poor (NSE <0.63). However, in scenarios which solely use anthropogenic disturbance factors and both use meteorological and anthropogenic disturbance factors, the PMI-NARX coupling models exhibit good prediction performance (NSE and R are all greater than 0.8). Especially under solely considering anthropogenic disturbance factors scenario, the model still exhibited good prediction accuracy with a negligible number of input variables. The results can provide technical and theoretical support for the prediction of groundwater level in other mining areas.