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<p>Floods present a critical global challenge, highlighting the need for comprehensive and standardized flood risk assessment frameworks. This study addresses the gaps in evaluating flood risk by integrating geospatial analysis with advanced flood modeling to assess hazard, exposure, and vulnerability. The flood hazard is analyzed through dynamic susceptibility (0.09 to 0.63), flood depth, and duration metrics. Exposure metrics incorporate population density and soil moisture variability, while the vulnerability assessment includes socioeconomic indicators such as accessibility, walkability, and thermal stress. Utilizing a recurrent neural network model, this study evaluates hazard levels (0.27 to 0.75), exposure (0.05 to 0.79), and vulnerability (0.08 to 0.72) to derive a comprehensive flood risk index ranging from 0.12 to 0.71. The results indicate that high-risk areas constitute 68.4% of the Kosi River basin, highlighting the urgent need for targeted interventions. This integrative approach provides a standardized methodology for flood risk assessment and equips decision-makers with actionable insights to enhance resilience through improved urban planning, early warning systems, and robust infrastructure development in vulnerable regions.</p>

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Comprehensive Flood Risk Assessment Using an Integrated Approach: A Study on Kosi River Basin Bihar, India

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Saketh Shetty T,
  • Karnam Prashanth,
  • Amba Shetty

摘要

Floods present a critical global challenge, highlighting the need for comprehensive and standardized flood risk assessment frameworks. This study addresses the gaps in evaluating flood risk by integrating geospatial analysis with advanced flood modeling to assess hazard, exposure, and vulnerability. The flood hazard is analyzed through dynamic susceptibility (0.09 to 0.63), flood depth, and duration metrics. Exposure metrics incorporate population density and soil moisture variability, while the vulnerability assessment includes socioeconomic indicators such as accessibility, walkability, and thermal stress. Utilizing a recurrent neural network model, this study evaluates hazard levels (0.27 to 0.75), exposure (0.05 to 0.79), and vulnerability (0.08 to 0.72) to derive a comprehensive flood risk index ranging from 0.12 to 0.71. The results indicate that high-risk areas constitute 68.4% of the Kosi River basin, highlighting the urgent need for targeted interventions. This integrative approach provides a standardized methodology for flood risk assessment and equips decision-makers with actionable insights to enhance resilience through improved urban planning, early warning systems, and robust infrastructure development in vulnerable regions.