The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods

Abstract

Flood is one of the most common natural disasters worldwide. The aim of this study was to evaluate the application of the Dempster–Shafer-based evidential belief function (EBF) for spatial prediction of flood-susceptible areas in Brisbane, Australia. This algorithm has been tested in landslide and groundwater mapping; however, it has not been examined in flood susceptibility modelling. EBF has an advantage over other statistical methods through its capability of evaluating the impacts of all classes of every flood-conditioning factor on flooding and assessing the correlation between each factor and flooding. EBF outcomes were compared with the results of well-known statistical methods, including logistic regression (LR) and frequency ratio (FR). Flood-conditioning factor data set consisted of elevation, aspect, plan curvature, slope, topographic wetness index (TWI), geology, stream power index (SPI), soil, land use/cover, rainfall, distance from roads and distance from rivers. EBF produced the highest prediction rate (82.60%) among all the methods. The research findings may provide a useful methodology for natural hazard and land use management.