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Interpretable and explainable AI (XAI) model for spatial drought prediction.

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  • 1Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia.

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Summary
This summary is machine-generated.

Accurate drought prediction is complex due to climate change. This study uses explainable AI (XAI) to show that including climatic variables significantly improves drought forecasting models, aligning with physical insights.

Keywords:
AustraliaDeep learningDrought predictionExplainable AIGISHybrid model

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Area of Science:

  • Environmental Science
  • Climate Science
  • Artificial Intelligence

Background:

  • Drought prediction is challenging due to its complex nature and the impact of climate change.
  • Traditional models often overlook the significant influence of climatic variables on drought events.
  • Existing accuracy assessments rely heavily on statistical metrics, limiting deeper understanding.

Purpose of the Study:

  • To investigate the impact of climatic variables on drought prediction using explainable artificial intelligence (XAI).
  • To compare data-driven models enhanced with climatic variables against physical-based model understandings.
  • To explore spatio-temporal interactions among predictors for various drought conditions.

Main Methods:

  • Development of an explainable deep learning model.
  • Application of SHapley Additive exPlanations (SHAP) for model interpretability.
  • Analysis of the Standard Precipitation Index (SPI) at a 12-month scale across five regions in New South Wales, Australia.

Main Results:

  • SHAP analysis revealed the critical importance of climatic variables at monthly and annual scales for drought prediction.
  • The explainable model successfully elucidated local interactions between predictors for different drought scenarios.
  • Results demonstrated strong alignment between XAI findings and established physical model interpretations.

Conclusions:

  • Climatic variables are essential predictors for enhancing drought forecasting accuracy.
  • XAI provides valuable insights into the mechanisms driving drought prediction models.
  • Integrating climatic variables and XAI offers a more robust approach to understanding and predicting drought events.