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http://dx.doi.org/10.3741/JKWRA.2020.53.2.121

The probabilistic estimation of inundation region using a multiple logistic regression analysis  

Jung, Minkyu (Department of Civil and Environmental Engineering, Sejong University)
Kim, Jin-Guk (Department of Civil and Environmental Engineering, Sejong University)
Uranchimeg, Sumiya (Department of Civil and Environmental Engineering, Sejong University)
Kwon, Hyun-Han (Department of Civil and Environmental Engineering, Sejong University)
Publication Information
Journal of Korea Water Resources Association / v.53, no.2, 2020 , pp. 121-129 More about this Journal
Abstract
The increase of impervious surface and development along the river due to urbanization not only causes an increase in the number of associated flood risk factors but also exacerbates flood damage, leading to difficulties in flood management. Flood control measures should be prioritized based on various geographical information in urban areas. In this study, a probabilistic flood hazard assessment was applied to flood-prone areas near an urban river. Flood hazard maps were alternatively considered and used to describe the expected inundation areas for a given set of predictors such as elevation, slope, runoff curve number, and distance to river. This study proposes a Bayesian logistic regression-based flood risk model that aims to provide a probabilistic risk metric such as population-at-risk (PAR). Finally, the logistic regression model demonstrates the probabilistic flood hazard maps for the entire area.
Keywords
GIS; Bayesian inference; Flood hazard map; Logistic regression;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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