Development of the machine learning and deep learning models with SHAP strategy for predicting groundwater levels in South Korea.

In this research, the groundwater levels (GWLs) were predicted by employing machine learning (i.e., stochastic gradient boosting (SGB), random forest (RF), generalized regression neural networks (GRNN), and group of method data handling (GMDH)) and deep learning (i.e., deep echo state network (Deep ESN) and long short-term memory (LSTM)) based on three predictive scenarios, Jeju Island, South Korea. In scenario 01, GWLs in Bongseong well was calculated utilizing rainfall, air temperature, relative humidity, wind speed, and various GWLs in different wells. Based on scenario 02, GWLs in Bongseong well was calculated using rainfall, air temperature, relative humidity, wind speed, and groundwater data (i.e., temperature, electric conductivity, and pressure). Finally, considering scenario 03, GWLs in Bongseong well were calculated by employing rainfall, air temperature, relative humidity, wind speed, and GWLs from 1-day to 15-day lead time. Five evaluation measures, including root mean squared error (RMSE), correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), relative error (RE), and root relative squared error (RRSE), were reflected for the predictive accuracy of developed models. Results showed that RF3 (RMSE = 0.053 m, CC = 1.000, NSE = 1.000, RE = 1.114, and RRSE = 0.013) based on scenario 03 performed the best predictive accuracy in GWLs of Bongseong well. Furthermore, the additional contributions of this research were achieved by the enhanced comparative evaluation through the SHapley Additive exPlanations (SHAP) strategy and one-way Analysis of Variance (ANOVA) test. The sensitivity analysis utilizing the SHAP strategy determined the significant feature indicator (i.e., GWL in 1-day lead-time) explaining its contribution to the predictive ability of developed models. The results of one-way ANOVA test provided that the predicted values were extracted from the same population as the measured values based on all models in scenario 03.