Characterizing groundwater distribution potential using GIS-based machine learning model in Chihe River basin, China

Mapping of groundwater distribution potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. A total of 245 wells were identified based on field survey in the Chihe River basin in Anhui province, out of which 172 wells locations were randomly used for training the machine models and the other 73 wells for validation process of machine models. Thirteen environmental variables including elevation, slope, slope aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI) were used to build the spatial database of this research. Three GIS-based machine learning models were used for mapping the groundwater distribution potential: logistic regression (LR), deep neural networks (DNN) and random forest (RF). Then, the applicability of those models was evaluated by the evaluation index of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R). The final results indicated that the potential of regional groundwater distribution is concentrated in moderate to high potential areas. Among them, the moderate to the high potential distribution area in the LR model accounted for 81.14% of the total area, 90.36% and 87.55% in the DNN model and the RF model, respectively. In addition, three machine learning models can be implemented for prediction of groundwater distribution based on the three evaluation indexes, among which the LR model performs more prominently. The good prediction capabilities of machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater distribution and management of water resources.