Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning model
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 interpretability meth...
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| Published in | Scientific reports Vol. 15; no. 1; pp. 20981 - 14 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.07.2025
Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-025-05316-2 |
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| 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 interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.922, F1-score: 0.84). (2) SHAP analysis revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales (SPEI12 and SPEI24) were key predictors, with long-term meteorological drought causing delayed groundwater drought, exacerbated by over-extraction and urbanization. The local SHAP values confirmed the robust importance of long-term meteorological drought and precipitation, and identified the interaction between precipitation and meteorological factors responsible for groundwater drought. (3) Future projections under the SSP5-8.5 climate scenario indicated a significant increase in drought-affected areas, with earlier onset, broader extent, and greater severity. This work provides a machine learning framework for evaluating groundwater drought characteristics driven by multiple factors. |
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| AbstractList | 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 interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.922, F1-score: 0.84). (2) SHAP analysis revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales (SPEI12 and SPEI24) were key predictors, with long-term meteorological drought causing delayed groundwater drought, exacerbated by over-extraction and urbanization. The local SHAP values confirmed the robust importance of long-term meteorological drought and precipitation, and identified the interaction between precipitation and meteorological factors responsible for groundwater drought. (3) Future projections under the SSP5-8.5 climate scenario indicated a significant increase in drought-affected areas, with earlier onset, broader extent, and greater severity. This work provides a machine learning framework for evaluating groundwater drought characteristics driven by multiple factors. 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 interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.922, F1-score: 0.84). (2) SHAP analysis revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales (SPEI12 and SPEI24) were key predictors, with long-term meteorological drought causing delayed groundwater drought, exacerbated by over-extraction and urbanization. The local SHAP values confirmed the robust importance of long-term meteorological drought and precipitation, and identified the interaction between precipitation and meteorological factors responsible for groundwater drought. (3) Future projections under the SSP5-8.5 climate scenario indicated a significant increase in drought-affected areas, with earlier onset, broader extent, and greater severity. This work provides a machine learning framework for evaluating groundwater drought characteristics driven by multiple factors. 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 interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.922, F1-score: 0.84). (2) SHAP analysis revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales (SPEI12 and SPEI24) were key predictors, with long-term meteorological drought causing delayed groundwater drought, exacerbated by over-extraction and urbanization. The local SHAP values confirmed the robust importance of long-term meteorological drought and precipitation, and identified the interaction between precipitation and meteorological factors responsible for groundwater drought. (3) Future projections under the SSP5-8.5 climate scenario indicated a significant increase in drought-affected areas, with earlier onset, broader extent, and greater severity. This work provides a machine learning framework for evaluating groundwater drought characteristics driven by multiple factors.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 interpretability method, to understand and predict the evolution of groundwater drought by evaluating eight models with SHAP analysis in the West Liao River Plain (WLRP), with a semi-arid climate. The research showed: (1) The XGBoost model, optimized by the Sparrow Search Algorithm (SSA), achieved the highest performance (AUC: 0.922, F1-score: 0.84). (2) SHAP analysis revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales (SPEI12 and SPEI24) were key predictors, with long-term meteorological drought causing delayed groundwater drought, exacerbated by over-extraction and urbanization. The local SHAP values confirmed the robust importance of long-term meteorological drought and precipitation, and identified the interaction between precipitation and meteorological factors responsible for groundwater drought. (3) Future projections under the SSP5-8.5 climate scenario indicated a significant increase in drought-affected areas, with earlier onset, broader extent, and greater severity. This work provides a machine learning framework for evaluating groundwater drought characteristics driven by multiple factors. |
| ArticleNumber | 20981 |
| Author | Yang, Liangping Gan, Zhiyuan Xie, Xianjun Ge, Weili Pan, Hongjie Su, Chunli |
| Author_xml | – sequence: 1 givenname: Zhiyuan surname: Gan fullname: Gan, Zhiyuan organization: School of Environmental Studies, China University of Geosciences, State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, Ministry of Ecology and Environment – sequence: 2 givenname: Xianjun surname: Xie fullname: Xie, Xianjun organization: School of Environmental Studies, China University of Geosciences, State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, Ministry of Ecology and Environment – sequence: 3 givenname: Chunli surname: Su fullname: Su, Chunli email: chl.su@cug.edu.cn organization: School of Environmental Studies, China University of Geosciences, State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, Ministry of Ecology and Environment – sequence: 4 givenname: Weili surname: Ge fullname: Ge, Weili organization: School of Environmental Studies, China University of Geosciences, Geological Survey Institute of Inner Mongolia Autonomous Region – sequence: 5 givenname: Hongjie surname: Pan fullname: Pan, Hongjie organization: Geological Survey Institute of Inner Mongolia Autonomous Region – sequence: 6 givenname: Liangping surname: Yang fullname: Yang, Liangping organization: Geological Survey Institute of Inner Mongolia Autonomous Region |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40594148$$D View this record in MEDLINE/PubMed |
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| Keywords | SHAP Groundwater Climate Change XGBoost Northern China Machine learning Meteorological drought |
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| Title | Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning model |
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