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Machine learning algorithms are an important tool in capturing complex relationships and modeling high-dimensional climate data and drought severities. The main innovation of this study is the evaluation of the accuracy and applicability of augmented tree and kernel-based machine learning algorithms in standardized precipitation evapotranspiration index (SPEI) prediction. Rainfall and temperature data obtained from the ONM Department of Chlef City in the Wadi Ouahrane basin (270 km2), from 1973 to 2017 were employed to predict SPEI at a monthly time scale. In the model’s setup, two different models, Gradient Boosted Trees (GBT) and Support vector machine (SVM), were employed. The determination coefficients (R2), root mean square error (RMSE), mean absolute error (MAE) were employed to evaluate the model performances. In terms of accuracy and efficiency, the SVM model (R2 = 0.957, RMSE = 0.216, and MAE = 0.142) exhibits superiority over the GBT (R2 = 0.870, RMSE = 0.369, and MAE = 0.293) for SPEI estimation. Therefore, SVM model has been proposed for drought prediction at various time scales. The results of the study contribute to the understanding of drought dynamics and decision-making by policy makers in water resources management.

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Modeling of Meteorological Drought Under Climate Variability Using the Standardizes Precipitation Evaporation Index (SPEI) and Machines Learning Models for Wadi Ouahrane Basin, Algeria

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammed Achite,
  • Somayeh Emami,
  • Okan Mert Katipoğlu,
  • Abderrezak Kamel Toubal

摘要

Machine learning algorithms are an important tool in capturing complex relationships and modeling high-dimensional climate data and drought severities. The main innovation of this study is the evaluation of the accuracy and applicability of augmented tree and kernel-based machine learning algorithms in standardized precipitation evapotranspiration index (SPEI) prediction. Rainfall and temperature data obtained from the ONM Department of Chlef City in the Wadi Ouahrane basin (270 km2), from 1973 to 2017 were employed to predict SPEI at a monthly time scale. In the model’s setup, two different models, Gradient Boosted Trees (GBT) and Support vector machine (SVM), were employed. The determination coefficients (R2), root mean square error (RMSE), mean absolute error (MAE) were employed to evaluate the model performances. In terms of accuracy and efficiency, the SVM model (R2 = 0.957, RMSE = 0.216, and MAE = 0.142) exhibits superiority over the GBT (R2 = 0.870, RMSE = 0.369, and MAE = 0.293) for SPEI estimation. Therefore, SVM model has been proposed for drought prediction at various time scales. The results of the study contribute to the understanding of drought dynamics and decision-making by policy makers in water resources management.