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Related Experiment Video

Updated: Sep 17, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Understanding the evolutionary processes and causes of groundwater drought using an interpretable machine learning

Zhiyuan Gan1,2, Xianjun Xie1,2, Chunli Su3,4

  • 1School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.

Scientific Reports
|July 2, 2025
PubMed
Summary

Groundwater drought prediction is improved using machine learning and SHAP analysis, identifying long-term meteorological drought as a key driver. Future climate scenarios project increased drought severity and extent.

Keywords:
Climate ChangeGroundwaterMachine learningMeteorological droughtNorthern ChinaSHAPXGBoost

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Area of Science:

  • Hydrology
  • Climate Science
  • Machine Learning

Background:

  • Groundwater drought assessment is challenging due to limited direct observation.
  • Understanding groundwater drought evolution is crucial for water resource management.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting groundwater drought.
  • To identify key factors influencing groundwater drought using SHAP analysis.
  • To project future groundwater drought trends under climate change scenarios.

Main Methods:

  • Employed machine learning models, including XGBoost optimized by the Sparrow Search Algorithm (SSA).
  • Utilized Shapley Additive Explanation (SHAP) for model interpretability and feature importance analysis.
  • Evaluated eight models for groundwater drought prediction in the West Liao River Plain (WLRP).

Main Results:

  • The SSA-optimized XGBoost model demonstrated high performance (AUC: 0.922, F1-score: 0.84).
  • Standardized Precipitation Evapotranspiration Index (SPEI) at 12- and 24-month scales were identified as key predictors.
  • Long-term meteorological drought, over-extraction, and urbanization were found to exacerbate groundwater drought.

Conclusions:

  • Machine learning and SHAP analysis provide a robust framework for understanding groundwater drought.
  • Long-term meteorological drought significantly impacts groundwater drought, with interactions from other factors.
  • Future climate change (SSP5-8.5) is projected to increase the frequency, extent, and severity of groundwater drought.