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Article

Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

1
Urban Flood Research Institute, University of Seoul, Seoul 02504, Korea
2
Water Resources Environment Research Institute, K-Water Research Institute, Daejeon 34045, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Gwo-Fong Lin
Water 2022, 14(2), 187; https://doi.org/10.3390/w14020187
Received: 24 November 2021 / Revised: 25 December 2021 / Accepted: 6 January 2022 / Published: 10 January 2022
Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics. View Full-Text
Keywords: urban flood forecasting and warning; deep learning; rainfall-runoff model; urban stream; model-driven; data-driven urban flood forecasting and warning; deep learning; rainfall-runoff model; urban stream; model-driven; data-driven
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MDPI and ACS Style

Won, Y.-M.; Lee, J.-H.; Moon, H.-T.; Moon, Y.-I. Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul. Water 2022, 14, 187. https://doi.org/10.3390/w14020187

AMA Style

Won Y-M, Lee J-H, Moon H-T, Moon Y-I. Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul. Water. 2022; 14(2):187. https://doi.org/10.3390/w14020187

Chicago/Turabian Style

Won, Yong-Man, Jung-Hwan Lee, Hyeon-Tae Moon, and Young-Il Moon. 2022. "Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul" Water 14, no. 2: 187. https://doi.org/10.3390/w14020187

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