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Scenario-Based Real-Time Flood Prediction with Logistic Regression

Korea Institute of Civil Engineering and Building Technology Goyangdaero 283, Ilsanseo-Gu, Goyang-Si 10223, Korea
Department of Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Astray
Water 2021, 13(9), 1191;
Received: 6 April 2021 / Revised: 19 April 2021 / Accepted: 21 April 2021 / Published: 25 April 2021
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology)
This study proposed a real-time flood extent prediction method to shorten the time it takes from the flood occurrence to an alert issuance. This method uses logistic regression to generate a flood probability discriminant for each grid constituting the study area, and then predicts the flood extent with the amount of runoff caused by rainfall. In order to generate the flood probability discriminant for each grid, a two-dimensional (2D) flood inundation model was verified by applying the Typhoon Chaba, which caused great damage to the study area in 2016. Then, 100 probability rainfall scenarios were created by combining the return period, duration, and time distribution using past observation rainfall data, and rainfall-runoff–inundation relation databases were built for each scenario by applying hydrodynamic and hydrological models. A flood probability discriminant based on logistic regression was generated for each grid by using whether the grid was flooded (1 or 0) for the runoff amount in the database. When the runoff amount is input to the generated discriminant, the flood probability on the target grid is calculated by the coefficients, so that the flood extent is quickly predicted. The proposed method predicted the flood extent in a few seconds in both cases and showed high accuracy with 83.6~98.4% and 74.4~99.1%, respectively, in the application of scenario rainfall and actual rainfall. View Full-Text
Keywords: real-time; flood extent prediction; logistic regression; scenario-based; database real-time; flood extent prediction; logistic regression; scenario-based; database
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MDPI and ACS Style

Lee, J.; Kim, B. Scenario-Based Real-Time Flood Prediction with Logistic Regression. Water 2021, 13, 1191.

AMA Style

Lee J, Kim B. Scenario-Based Real-Time Flood Prediction with Logistic Regression. Water. 2021; 13(9):1191.

Chicago/Turabian Style

Lee, Jaeyeong; Kim, Byunghyun. 2021. "Scenario-Based Real-Time Flood Prediction with Logistic Regression" Water 13, no. 9: 1191.

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