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<p>Flood rank among the most destructive natural disasters, and their impact is being exacerbated by climate change and rapid global urbanization, which have led to a growing number of people residing in disaster-prone areas. Early assessment and mitigation of flood risks are crucial for reducing vulnerability. In response, there has been a marked increase in flood studies leveraging remote sensing and Geographic Information System (GIS) technologies. This review article aims to explore risk, flood susceptibility, and the knowledge gaps in flood hazard management, with a focus on applying machine learning techniques. It offers a comprehensive overview of published studies that utilize machine learning algorithms for spatial flood susceptibility mapping. Through an extensive global analysis, we compare various machine learning methods, highlighting their accuracy, strengths, and limitations based on specific evaluation criteria. Among these, tree-based ensemble algorithms consistently demonstrate superior performance compared to other machine learning approaches.</p> Graphical Abstract <p></p>

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A review of the literature on spatial prediction of floods using machine learning models

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Sonia Hajji,
  • Abdelghani Boudhar,
  • Abdenbi Elaloui,
  • Abdelaziz Merghadi,
  • Samira Krimissa,
  • Mustapha Namous,
  • Hasna Eloudi,
  • Maryam Ismaili,
  • Meryem El Bouzekraoui

摘要

Flood rank among the most destructive natural disasters, and their impact is being exacerbated by climate change and rapid global urbanization, which have led to a growing number of people residing in disaster-prone areas. Early assessment and mitigation of flood risks are crucial for reducing vulnerability. In response, there has been a marked increase in flood studies leveraging remote sensing and Geographic Information System (GIS) technologies. This review article aims to explore risk, flood susceptibility, and the knowledge gaps in flood hazard management, with a focus on applying machine learning techniques. It offers a comprehensive overview of published studies that utilize machine learning algorithms for spatial flood susceptibility mapping. Through an extensive global analysis, we compare various machine learning methods, highlighting their accuracy, strengths, and limitations based on specific evaluation criteria. Among these, tree-based ensemble algorithms consistently demonstrate superior performance compared to other machine learning approaches.

Graphical Abstract