ABSTRACT Accurate mapping of depth to bedrock (DTB) in complex post‐glacial landscapes is challenging due to high spatial variability and the prevalence of bedrock outcrops, which introduce “structural zeros” that violate standard regression modelling assumptions. To address this, we developed a two‐part machine learning framework that separates bedrock outcrop classification from continuous depth prediction and applied it to a Swedish case study. The binary classifier effectively distinguished outcrops from sediment‐covered areas (AUC = 0.96, F1‐score = 0.83), whereas the regression component provided reliable DTB estimates in non‐outcrop areas ( R 2 = 0.68, RMSE = 5.74 m). The final fused model ( R 2 = 0.67, RMSE = 5.80 m) outperformed both the existing national Inverse Distance Weighting interpolation model ( R 2 = 0.61, RMSE = 6.61 m) and a global model evaluated over the study area ( R 2 = 0.23, RMSE = 9.03 m). The two‐part model remains robust in data‐sparse regions. However, a depth‐stratified uncertainty analysis revealed miscalibration in the uncertainty estimates of the regression component: in shallow ranges (2–15 m), the model overestimates uncertainty and produces overly wide prediction intervals. In deep ranges (> 30 m), it underestimates uncertainty while systematically underpredicts (mean error = 12.44 m). Our findings emphasize that zero‐inflated datasets require special consideration in modeling approaches, and that depth‐stratified evaluation is essential for understanding model reliability.
Lin et al. (Sun,) studied this question.