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<p>Drought is a critical barrier to socioeconomic development, necessitating effective modeling to mitigate its impacts. Drought vulnerability modeling is crucial for managing and lessening the effects of drought. Creating a drought vulnerability map is the first step in developing a comprehensive drought management strategy, which is crucial for reducing the likelihood of droughts. This study introduces a novel Combined Drought Index (CDI) developed through a weighted Entropy-based TOPSIS method to facilitate comprehensive drought analysis. Building on this, we employed a suite of ensemble machine learning techniques, including M5P, Dagging, Random SubSpace (RSS), Random Forest (RF), and Support Vector Machine (SVM) models, to evaluate drought vulnerability maps (DVMs) across Pakistan. The models were trained on 70% of the data, with the remaining 30% reserved for validation. Performance was assessed using RMSE, MSE, MAE, and R-square metrics. Among the models, the SVM exhibited the highest accuracy in capturing drought vulnerability, suggesting its potential for reliable spatial assessment.</p> Graphical Abstract <p></p>

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A Data-Driven Approach to Spatial Drought Mapping Using Machine Learning

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Farheen Fatima,
  • Ijaz Hussain,
  • Hanen Louati,
  • Jianyi Lin,
  • Ibrahim A. Nafisah,
  • Mohammed M. A. Almazah

摘要

Drought is a critical barrier to socioeconomic development, necessitating effective modeling to mitigate its impacts. Drought vulnerability modeling is crucial for managing and lessening the effects of drought. Creating a drought vulnerability map is the first step in developing a comprehensive drought management strategy, which is crucial for reducing the likelihood of droughts. This study introduces a novel Combined Drought Index (CDI) developed through a weighted Entropy-based TOPSIS method to facilitate comprehensive drought analysis. Building on this, we employed a suite of ensemble machine learning techniques, including M5P, Dagging, Random SubSpace (RSS), Random Forest (RF), and Support Vector Machine (SVM) models, to evaluate drought vulnerability maps (DVMs) across Pakistan. The models were trained on 70% of the data, with the remaining 30% reserved for validation. Performance was assessed using RMSE, MSE, MAE, and R-square metrics. Among the models, the SVM exhibited the highest accuracy in capturing drought vulnerability, suggesting its potential for reliable spatial assessment.

Graphical Abstract