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[Display omitted] • Epikarst thickness in depression is markedly greater than that on hillslope. • Spherical model fits for the interpolation of spatial structure of epikarst thickness. • Soil thickness is identified as the primary control on epikarst thickness. • ANNs model is suitable for predicting epikarst thickness based on RFE for variable selection. Epikarst, as a distinct layer of karst critical zone, plays a crucial role in water storage-transfer functions and biogeochemical processes. The epikarst thickness, which shows high spatial variability, is an important parameter that controls the dynamic characteristics of the water cycle. Due to the time-cost constraints in conventional field surveys and coarse resolution of remote sensing interpretation, empirical statistical models based on epikarst forming factors are an alternative to quantify the epikarst thickness. In this study, we investigated the spatial variability of epikarst thickness in Huanjiang, Guangxi of southwest China, based on electrical resistivity tomography with 36 sampling lines (1,658 sampling points) in a dolomite catchment. The controlling factors of epikarst thickness were defined based on selecting terrain-vegetation-soil variables with GLM (generalized linear model), RFE (recursive feature elimination) and PLSR (partial least squares regression) methods. Then, the best method for predicting epikarst thickness was selected based on the model with the highest accuracy. Results showed that the mean epikarst thickness was 6.32 m; the epikarst was significantly thicker in the depression (7.2 m) than on the hill slope (5.8 m), whereas there were no significant differences among slope positions. Geostatistical analysis showed that the spherical model reflected the strong spatial autocorrelation of epikarst thickness. Based on the prediction performance of the models, the terrain-vegetation variables alone could not explain epikarst thickness satisfactorily (explanation rate, 32.9 %; testing accuracy, R2 <0.5). By contrast, the modeling accuracy significantly increased (64.7 %, 0.7) when soil properties were considered as predictors. Based on validation criteria (R2 and RMSE), RFE for selecting variables and ANNs for prediction (0.814, 2.137 m) were the optimal model set to predict the epikarst thickness. This study provides a reference method for predicting epikarst thickness at a hillslope scale and the information would be helpful for hydrological catchment modeling. [ABSTRACT FROM AUTHOR]