Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment

Abstract

For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions.