Scenario-Based Real-Time Flood Prediction with Logistic Regression

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...

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Bibliographic Details
Published inWater (Basel) Vol. 13; no. 9; p. 1191
Main Authors Lee, Jaeyeong, Kim, Byunghyun
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2021
Subjects
Analysis
Classification
Flood damage
flood extent prediction
Flood forecasting
Flood predictions
Floods
Forecasting
Generalized linear models
Hydraulics
Hydrologic data
Hydrologic models
Hydrology
logistic regression
Logistics
Neural networks
Parameter estimation
Probability
Rain and rainfall
Rainfall-runoff relationships
Real time
Regression analysis
Rivers
Runoff
scenario-based
Statistical analysis
Storm damage
Topography
Two dimensional models
Typhoons
Urban areas
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Summary: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.
ISSN:2073-4441
2073-4441
DOI:10.3390/w13091191