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<p>Flooding, intensified by climate change and urbanisation, poses a major global threat, especially in developing countries. Traditional flood risk assessments are often costly and limited in scope. This study addresses these issues by employing space-based technologies: Satellite Remote Sensing (SRS) for rapid, wide-area data acquisition and Geographic Information Systems (GIS) for spatial analysis. This study employs a robust Multi-Criteria Decision Analysis with a novel hybrid weighting approach, integrating subjective expert judgment and objective data-driven analysis, to assess flood risk. Data processing included slope analysis, proximity-to-streams assessment, land-cover classification, and rainfall analysis. A composite flood risk map was generated, categorising the region into low, medium, and high-risk zones. The ROC and AUC (0.78) were utilised to assess the model’s performance. Results identified low-risk areas (24% overall), medium-risk areas (67%) and high-risk areas (9%), mainly low-lying regions near rivers and steep slopes. Urban land cover increased runoff and flood susceptibility compared to forested areas. This study offers a transferable and cost-effective flood risk assessment methodology applicable to similar data-scarce developing regions. Its findings are crucial for policymakers and directly support global objectives, such as the United Nations Sustainable Development Goals and the Sendai Framework, thereby enhancing resilience and informing proactive disaster management internationally.</p>

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Space based multicriteria analytics with hybrid weighting for flood risk management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月22日

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  • Kenneth Uchua,
  • John Essien,
  • Adamson Oloyede,
  • Funmilola Oluwafemi,
  • Williams Adaji-Agbane,
  • Uche Ekeson,
  • Dapo Olatunbosun,
  • Chiemeka Nsofor,
  • Nazifa Bauka,
  • Tijesuni Ogunrombi

摘要

Flooding, intensified by climate change and urbanisation, poses a major global threat, especially in developing countries. Traditional flood risk assessments are often costly and limited in scope. This study addresses these issues by employing space-based technologies: Satellite Remote Sensing (SRS) for rapid, wide-area data acquisition and Geographic Information Systems (GIS) for spatial analysis. This study employs a robust Multi-Criteria Decision Analysis with a novel hybrid weighting approach, integrating subjective expert judgment and objective data-driven analysis, to assess flood risk. Data processing included slope analysis, proximity-to-streams assessment, land-cover classification, and rainfall analysis. A composite flood risk map was generated, categorising the region into low, medium, and high-risk zones. The ROC and AUC (0.78) were utilised to assess the model’s performance. Results identified low-risk areas (24% overall), medium-risk areas (67%) and high-risk areas (9%), mainly low-lying regions near rivers and steep slopes. Urban land cover increased runoff and flood susceptibility compared to forested areas. This study offers a transferable and cost-effective flood risk assessment methodology applicable to similar data-scarce developing regions. Its findings are crucial for policymakers and directly support global objectives, such as the United Nations Sustainable Development Goals and the Sendai Framework, thereby enhancing resilience and informing proactive disaster management internationally.