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Floods are the most devastating hydro-meteorological hazard, having significant socio-economic and environmental impacts, particularly in data-scarce regions where traditional risk assessment methods are often impractical. This study addresses the challenge of assessing flood susceptibility in the Upper Indus River Basin (UIRB), a region characterized by complex topography, limited data availability, and heightened vulnerability due to climate change. Integrating Analytical Hierarchy Process (AHP) with Geographic Information System (GIS) and Remote Sensing (RS) techniques, current study develops a comprehensive framework for flood risk assessment. The methodology leverages key indicators including topography, land use, precipitation, and river proximity, assigning weights through pairwise comparisons to generate a detailed flood susceptibility map. The accuracy was validated using area Under the Curve (AUC) approach, achieving a value of 0.81, indicating high predictive reliability. The results highlight those low-lying areas, regions with high precipitation and proximity to rivers are most susceptible to flooding. This study offers a scalable, cost-effective solution for flood risk assessment in data-scarce environments, providing valuable insights for disaster management practitioners and policymakers to enhance flood mitigation strategies.

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Assessing Flood Susceptibility in Data-Scare Regions of Upper Indus River Basin Using AHP and Remote Sensing Approaches

  • 章节

  • 发表日期：

    2026年

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  • Muhammad Sajid Mehmood,
  • Zhai Shiyan,
  • Muhammad Irfan Ahamad,
  • Sohail Abbas,
  • Adnan ul Rehman,
  • Qin Yaochen

摘要

Floods are the most devastating hydro-meteorological hazard, having significant socio-economic and environmental impacts, particularly in data-scarce regions where traditional risk assessment methods are often impractical. This study addresses the challenge of assessing flood susceptibility in the Upper Indus River Basin (UIRB), a region characterized by complex topography, limited data availability, and heightened vulnerability due to climate change. Integrating Analytical Hierarchy Process (AHP) with Geographic Information System (GIS) and Remote Sensing (RS) techniques, current study develops a comprehensive framework for flood risk assessment. The methodology leverages key indicators including topography, land use, precipitation, and river proximity, assigning weights through pairwise comparisons to generate a detailed flood susceptibility map. The accuracy was validated using area Under the Curve (AUC) approach, achieving a value of 0.81, indicating high predictive reliability. The results highlight those low-lying areas, regions with high precipitation and proximity to rivers are most susceptible to flooding. This study offers a scalable, cost-effective solution for flood risk assessment in data-scarce environments, providing valuable insights for disaster management practitioners and policymakers to enhance flood mitigation strategies.