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<p>Local Intensive Precipitation (LIP), intensified by climate change, has increasingly caused severe urban flooding. Although traditional hydrodynamic models such as SWMM and FLO-2D offer high accuracy in flood prediction, their computational demands hinder real-time application. This study introduces a rapid flood depth prediction model based on a Support Vector Machine (SVM), trained with data generated from a physically-based 1D–2D coupled simulation. The target area is the Jinheung Apartment intersection in Gangnam, Seoul—an area highly prone to flooding. Cumulative rainfall and manhole overflow data from 1 to 5&#xa0;h scenarios were used as input variables to predict flood depth. Model validation consisted of two parts: (1) the 1D–2D hydrodynamic model (SWMM–FLO-2D) was validated using observed flood records from September 21, 2010, achieving a 64% match with NDMS inundation points. (2) The trained SVM model was verified by comparing its predictions against FLO-2D results generated using a 3-hour Huff-distributed rainfall scenario. The SVM model showed strong performance with <i>R</i><sup>2</sup> = 0.988, NSE = 0.987, % difference = 1.080, and RMSE = 0.098 m. The results confirm that integrating machine learning with physical simulation can provide fast and reliable flood predictions, supporting timely disaster response in urban areas.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

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  • Beom-Jin Kim,
  • Minkyu Kim,
  • Jaehwan Yoo,
  • Byunghyun Kim

摘要

Local Intensive Precipitation (LIP), intensified by climate change, has increasingly caused severe urban flooding. Although traditional hydrodynamic models such as SWMM and FLO-2D offer high accuracy in flood prediction, their computational demands hinder real-time application. This study introduces a rapid flood depth prediction model based on a Support Vector Machine (SVM), trained with data generated from a physically-based 1D–2D coupled simulation. The target area is the Jinheung Apartment intersection in Gangnam, Seoul—an area highly prone to flooding. Cumulative rainfall and manhole overflow data from 1 to 5 h scenarios were used as input variables to predict flood depth. Model validation consisted of two parts: (1) the 1D–2D hydrodynamic model (SWMM–FLO-2D) was validated using observed flood records from September 21, 2010, achieving a 64% match with NDMS inundation points. (2) The trained SVM model was verified by comparing its predictions against FLO-2D results generated using a 3-hour Huff-distributed rainfall scenario. The SVM model showed strong performance with R2 = 0.988, NSE = 0.987, % difference = 1.080, and RMSE = 0.098 m. The results confirm that integrating machine learning with physical simulation can provide fast and reliable flood predictions, supporting timely disaster response in urban areas.