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Regional agricultural drought vulnerability prediction based on interpretable Random Forest.

Dang Luo1, Xinqing Qiao2

  • 1School Statistics and Mathematics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, P.R. China.

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Summary

This study predicts drought vulnerability in Henan Province using the Random Forest model, finding an increasing trend and recommending enhanced early warning systems. Accurate predictions aid in mitigating drought

Keywords:
Drought vulnerabilityGrey correlation analysisRandom Forest algorithmSHapley Additive exPlanations model

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Area of Science:

  • Environmental Science
  • Climate Science
  • Disaster Management

Background:

  • Drought is a major global natural disaster impacting societies and economies.
  • Henan Province faces significant socio-economic risks due to drought.
  • Predicting drought vulnerability is crucial for mitigation and stability.

Purpose of the Study:

  • To predict the drought vulnerability index (DVI) for Henan Province.
  • To identify key factors influencing drought vulnerability.
  • To inform timely measures for drought impact mitigation.

Main Methods:

  • Calculated historical DVI (2010-2022) for Henan Province.
  • Established a prediction indicator system using grey correlation analysis.
  • Applied Random Forest and SHapley Additive exPlanations (SHAP) models for prediction and interpretation.

Main Results:

  • Random Forest model achieved 96.96% accuracy with 3.05% Average Percentage Error.
  • Predicted an increasing trend in drought vulnerability for Henan Province (2023-2025).
  • SHAP model provided interpretable insights into Random Forest model predictions.

Conclusions:

  • The interpretable Random Forest model accurately predicts drought vulnerability.
  • Proactive measures including enhanced early warning and improved resilience are recommended.
  • Reducing the socio-economic impact of drought is essential for sustainable development.