Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning model

Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning model

Abstract Drought is a widespread natural disaster, and rapid assessment of groundwater drought has become a challenge due to the lack of direct spatiotemporal observation of groundwater. We employed machine learning models and the Shapley Additive Explanation (SHAP), a game theory-based interpretabi...

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Bibliographic Details
Main Authors: Zhiyuan Gan, Xianjun Xie, Chunli Su, Weili Ge, Hongjie Pan, Liangping Yang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Groundwater
Machine learning
Meteorological drought
Climate Change
SHAP
XGBoost
Online Access:https://doi.org/10.1038/s41598-025-05316-2
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