Year: 2025
Type: article
Abstract: 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 R 2 = 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.
Source: Scientific Reports
Authors: Beom-Jin Kim, Beom-Jin Kim, Minkyu Kim, Jaehwan Yoo, Byunghyun Kim, Byunghyun Kim
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G4402568146, G8469824918
OpenAlex ID: W4413834581