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Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy

Quynh Duy Bui1, Chinh Luu2, Sy Hung Mai2

  • 1Faculty of Bridges and Roads, Hanoi University of Civil Engineering, Hanoi, Vietnam.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 11, 2022
PubMed
Summary

This study introduces a novel machine learning (ML) and analytic hierarchy process (AHP) approach for creating comprehensive flood risk maps. The integrated framework effectively assesses flood hazard, exposure, and vulnerability, identifying high-risk areas for better disaster management.

Keywords:
AHPFlood risk mapVietnamflood susceptibilitymachine learning

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Area of Science:

  • Environmental Science
  • Geographic Information Science
  • Artificial Intelligence

Background:

  • Flooding poses a significant threat in many regions, necessitating accurate risk assessment tools.
  • Existing flood risk assessment methods often lack integration of multiple crucial factors.
  • Developing effective flood risk maps is vital for disaster preparedness and mitigation.

Purpose of the Study:

  • To propose a novel, integrated framework for flood risk assessment using machine learning (ML) and the analytic hierarchy process (AHP).
  • To develop a holistic flood risk assessment map for Quang Binh province, Vietnam.
  • To compare the performance of various ML models in flood susceptibility mapping.

Main Methods:

  • Utilized machine learning techniques to create flood susceptibility maps.
  • Employed the analytic hierarchy process (AHP) to integrate flood vulnerability and exposure criteria.
  • Collected historical flood data and influencing factors (elevation, slope, land cover, rainfall, etc.) for model development and validation.
  • Integrated flood hazard, exposure, and vulnerability components into a final flood risk assessment framework.

Main Results:

  • Deep learning models demonstrated superior performance (AUC = 0.984) compared to other ensemble models.
  • The flood risk map identified specific areas with extremely high, high, medium, low, and very low risk levels.
  • Over 6% of the study area was categorized under extremely high or high flood risk, highlighting vulnerable zones.

Conclusions:

  • The integration of ML models with AHP provides a promising and effective framework for flood risk mapping in flood-prone regions.
  • The developed flood risk assessment map can aid policymakers and emergency managers in targeted interventions.
  • Accurate flood risk assessment is crucial for enhancing community resilience and reducing flood-related damages.