September 10, 2025Scientific ReportsOpen Access

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

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The support vector machine model achieved R2 = 0.988, indicating high accuracy in flood depth prediction.
Cumulative rainfall and manhole overflow data were used to predict flood depth for scenarios spanning 1 to 5 hours.
Model validation utilized a comparison with results from the SWMM-FLO-2D hydrodynamic model and observed flood records.
Integrating machine learning with physical simulation highlights the potential for timely disaster response during flooding events.

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Assessment of flood prediction accuracy with a support vector machine in urban areas, indicating enhanced disaster response.

Kim et al. (2025) studied this question.

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