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Rapid simulation for real-time flood depth prediction using support vector machine.

Beom-Jin Kim1, Minkyu Kim1, Jaehwan Yoo2

  • 1Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea.

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|August 29, 2025
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Summary
This summary is machine-generated.

This study developed a rapid flood depth prediction model using Support Vector Machine (SVM) for urban areas prone to flooding. The SVM model, trained with physical simulation data, offers fast and reliable predictions for timely disaster response.

Keywords:
Flood depthLIP (Local Intensive Precipitation)Rapid simulationReal-timeSVM (Support Vector Machine)

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Area of Science:

  • Environmental science
  • Hydrology
  • Urban planning

Background:

  • Climate change intensifies Local Intensive Precipitation (LIP), leading to severe urban flooding.
  • Traditional hydrodynamic models (SWMM, FLO-2D) are accurate but computationally intensive, limiting real-time flood prediction.
  • Urban areas like Gangnam, Seoul, face significant flood risks.

Purpose of the Study:

  • To develop a rapid flood depth prediction model for urban areas.
  • To integrate machine learning with physical simulations for enhanced flood forecasting.
  • To support timely disaster response in flood-prone urban environments.

Main Methods:

  • A Support Vector Machine (SVM) model was developed for rapid flood depth prediction.
  • The SVM model was trained using data generated from a 1D-2D coupled hydrodynamic simulation (SWMM-FLO-2D).
  • Input variables included cumulative rainfall and manhole overflow data over 1 to 5-hour scenarios.

Main Results:

  • The integrated SVM model demonstrated high performance with R² = 0.988, NSE = 0.987, % difference = 1.080, and RMSE = 0.098 m.
  • The 1D-2D hydrodynamic model (SWMM-FLO-2D) was validated against observed flood records with a 64% match.
  • The SVM model accurately predicted flood depths when compared to FLO-2D simulation results.

Conclusions:

  • Integrating machine learning with physical simulations offers a fast and reliable approach to flood prediction.
  • The developed SVM model can significantly aid in real-time urban flood risk management.
  • This approach enhances the efficiency of disaster response systems in urban areas facing climate change impacts.