| Summary: | Study region: Mu Us Sandy Land, China. Study focus: The groundwater table in unconfined aquifers is often a subdued replica of the topography or land surface. In hilly terrain, spatial interpolation methods are prone to errors such as higher interpolation water level than the ground surface due to sparse observation wells. Meanwhile, in ridge areas, groundwater depth can easily be overestimated with limited groundwater observations. To address these issues, this paper proposes a framework to learn groundwater depth using related groundwater variables based on the extreme gradient boosting (XGB) machine learning method. Moreover, the groundwater level map is drawn based on the predicted groundwater depths and elevation data.New Hydrological Insights for the RegionThe results obtained by the XGB algorithm are compared with those of common interpolation methods (including inverse distance weighting, ordinary kriging, and collaborative ordinary kriging) and machine learning methods (including multiple linear regression, geographically weighted regression, support vector regression, and random forest). The results showed that the XGB using the Tweedie loss function performs best in learning groundwater depth compared with interpolation methods and other machine learning methods, and almost completely overcomes the effects of imbalanced data. The topographic factors such as elevation, topographic position index, and topographic roughness index are key factors for groundwater depth. This study provides a reference for improving regional GL mapping, especially in areas of complex terrain.
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