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<p>Floods are among the most destructive natural disasters, posing significant risks to communities and infrastructure. In September 2014, torrential rains triggered severe flooding in the Jhelum River Basin in the Union Territory of Kashmir, India. This study focuses on locating regions that are at high risk of flooding within the basin and develop a flood susceptibility map by utilizing Geographic Information System (GIS) technology, Multi-Criteria Decision Analysis (MCDA), and the Analytical Hierarchy Process (AHP). The analysis involved ten spatial datasets, such as the Topographic Wetness Index (TWI), elevation, drainage density, proximity to rivers, and land use patterns. The datasets were weighted using MCDA and AHP to prioritize flood causative factors. The resulting flood susceptibility map indicated that 5% of the studied area is highly vulnerable to flooding, whereas 35% exhibits moderate susceptibility. These findings provide critical insights for decision-makers to enhance flood risk mitigation strategies in the region. The merger of GIS and AHP demonstrated a robust approach for flood susceptibility mapping in mountainous and flood-prone areas. Future research should explore the inclusion of real-time data and the impact of climate change on flood vulnerability.</p>

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Flood Susceptibility Mapping Using GIS with AHP and MCDA Technique for Jhelum River Basin, Union Territory of Kashmir, India

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Hashim Mohi u din Dar,
  • Danish manzoor Bhat,
  • Khalid hafiz Mir

摘要

Floods are among the most destructive natural disasters, posing significant risks to communities and infrastructure. In September 2014, torrential rains triggered severe flooding in the Jhelum River Basin in the Union Territory of Kashmir, India. This study focuses on locating regions that are at high risk of flooding within the basin and develop a flood susceptibility map by utilizing Geographic Information System (GIS) technology, Multi-Criteria Decision Analysis (MCDA), and the Analytical Hierarchy Process (AHP). The analysis involved ten spatial datasets, such as the Topographic Wetness Index (TWI), elevation, drainage density, proximity to rivers, and land use patterns. The datasets were weighted using MCDA and AHP to prioritize flood causative factors. The resulting flood susceptibility map indicated that 5% of the studied area is highly vulnerable to flooding, whereas 35% exhibits moderate susceptibility. These findings provide critical insights for decision-makers to enhance flood risk mitigation strategies in the region. The merger of GIS and AHP demonstrated a robust approach for flood susceptibility mapping in mountainous and flood-prone areas. Future research should explore the inclusion of real-time data and the impact of climate change on flood vulnerability.