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This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m 3 /d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

A dual‐porosity fractal model is derived that considers transient flow from block to fissure, skin between the fissure and the block, and storage capacity and skin effect on the pumping well. Type curves for different flow dimensions are provided. They concern the pumping well and observation wells located either in the fissure system or in a matrix block. The interpretation of a field case using this model shows good agreement between the theoretical drawdown and the observed data, with consistent results from the pumping well and the observation well. A flow dimension of 1.45 is obtained.