Proababilistic Approach to Deterministic Inundation Map Informed by Geographical Factors

Urbanization causes extension of impervious surface interrupting natural hydrological cycle, which may increase in the number of disaster factors causing difficulties in terms of flood management. Flood control measures should prioritize identification of areas where flooding is expected to occur, considering various spatial characteristics distributed over the areas at risk. In this study, a probabilistic flood risk assessment was performed. The flood hazard map for a 100-year return level was used to illustrate the concept of a probabilistic model. Here, we trained the model to obtain the relationship between the estimated inundation area and potential predictors such as elevation, slope, curve number, and distance to the river. In this study, a Bayesian logistic regression analysis was performed to impose probabilities on the inundation for each grid. Finally, the flood risk was provided with the population for the entire target area through the model.

 

Keywords: Bayesian Inference, Flood Hazard Map, Geographical Information, Logistic Regression

 

Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 19AWMP-B121100-04)