A residual correction approach for improving rainfall-runoff model performance in flood early warning systems

Haneul Lee, Seungmin Lee, Hoyong Lee, Narae Kang and Soojun Kim ()
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Haneul Lee: Inha University
Seungmin Lee: Inha University
Hoyong Lee: Inha University
Narae Kang: Korea Institute of Civil Engineering and Buliding Technology(KICT)
Soojun Kim: Inha University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 18, No 27, 21459-21482

Abstract: Abstract To improve flood forecasting accuracy, this study proposes a hybrid model that combines a physically based rainfall-runoff model with AI-based residual prediction results. The storage function model was used to simulate runoff, while AI models (random forests, support vector regression, long short term memory, and gated recurrent unit) were used to predict the residuals. The hybrid model calculates corrected runoff by combining simulated runoff from the storage function model with AI-predicted residuals. Compared with the standalone storage function model and AI models for runoff prediction, the hybrid model demonstrated effectiveness in predicting both peak discharge and the timing of peak discharge. The proposed hybrid model can improve flood forecasting reliability and offers a valuable tool for early warning and disaster management.

Keywords: Flood early warning; Hybrid model; Machine learning; Residual prediction; Storage function model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07639-9

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