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  11. Enhancing Flood Risk Assessment In Urban Areas By Integrating Hydrodynamic Models And Machine Learning Techniques

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques

Alireza Khoshkonesh1, Rouzbeh Nazari1, Mohammad Reza Nikoo2

  • 1Department of Civil Engineering, The University of Memphis, TN 38125, USA.

The Science of the Total Environment
|August 30, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances urban flood prediction by integrating hydrodynamic models with machine learning (ML). The advanced Extra Trees-Principal Component Analysis (ET-PCA) model achieved near-perfect accuracy, enabling precise flood risk zoning.

Area of Science:

  • Environmental Science
  • Hydrology
  • Urban Planning

Background:

  • Urban flood risks are increasing due to climate change and infrastructure development.
  • Innovative assessment approaches are crucial for effective urban flood management.
  • Existing methods require enhancement for accurate prediction and hazard analysis.

Purpose of the Study:

  • To integrate advanced hydrodynamic models with machine learning (ML) for improved urban flood prediction.
  • To enhance urban flood hazard analysis and risk zoning.
  • To develop a framework for increasing global urban flood resilience.

Main Methods:

  • Integration of 1D and 2D hydrodynamic models calibrated with precise parameters.
  • Simulation of flood scenarios using high-resolution Lidar data and sophisticated modeling.
Keywords:
Flood modelingGISHEC-seriesHazard analysis

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  • Application of advanced ML models, including Extra Trees-Principal Component Analysis (ET-PCA).
  • Incorporation of socioeconomic data for vulnerable zone mapping.

    • Main Results:

    • 2D hydrodynamic models showed superior accuracy in predicting flood dynamics.
    • The ET-PCA ML model achieved near-perfect predictive reliability (R² = 0.999).
    • Precise flood risk zoning was enabled by the ML model.
    • Socioeconomic data highlighted vulnerable urban areas, informing targeted mitigation strategies.

    Conclusions:

    • Combining hydrodynamic simulations with ML significantly enhances urban flood prediction accuracy.
    • The developed framework provides a reliable tool for urban flood risk assessment and management.
    • This approach supports urban planners and policymakers in devising effective flood mitigation and infrastructure resilience strategies.
    Lidar VHR satellite images
    Machine learning