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<p>Traditional drought indices, such as the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), often fail to capture the complexity of drought events, which involve multiple interacting variables. To address this gap, this study applies the Principle of Maximum Entropy (POME) copula to combine SPI and SRI into a Joint Deficit Index (JDI), offering a more complete assessment of hydrometeorological drought. We used machine learning models, including Random Forest (RF), Quantile Random Forest (QRF), Extreme Gradient Boosting (XGB), and Quantile Regression XGBoost (QXGB), to predict JDI, while also incorporating uncertainty analysis using the Uncertainty Estimation based on Local Errors and Clustering (UNEEC) method. This approach not only improves the accuracy of drought predictions but also quantifies the uncertainty of the models, enhancing reliability. Model performance, evaluated with <i>R</i><sup>2</sup>, RMSE, and MAE, showed XGB as the best performer, achieving <i>R</i><sup>2</sup> = 0.93 and RMSE = 0.16. This integration of multivariate drought indices, machine learning, and uncertainty analysis provides a more robust tool for drought monitoring and water resource management in arid regions.</p>

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Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Hossein Zamani,
  • Zohreh Pakdaman,
  • Marzieh Shakari,
  • Ommolbanin Bazrafshan,
  • Sajad Jamshidi

摘要

Traditional drought indices, such as the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), often fail to capture the complexity of drought events, which involve multiple interacting variables. To address this gap, this study applies the Principle of Maximum Entropy (POME) copula to combine SPI and SRI into a Joint Deficit Index (JDI), offering a more complete assessment of hydrometeorological drought. We used machine learning models, including Random Forest (RF), Quantile Random Forest (QRF), Extreme Gradient Boosting (XGB), and Quantile Regression XGBoost (QXGB), to predict JDI, while also incorporating uncertainty analysis using the Uncertainty Estimation based on Local Errors and Clustering (UNEEC) method. This approach not only improves the accuracy of drought predictions but also quantifies the uncertainty of the models, enhancing reliability. Model performance, evaluated with R2, RMSE, and MAE, showed XGB as the best performer, achieving R2 = 0.93 and RMSE = 0.16. This integration of multivariate drought indices, machine learning, and uncertainty analysis provides a more robust tool for drought monitoring and water resource management in arid regions.