Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods

Abstract

Statistical methods are the most popular techniques to model and map flood-prone areas. Although a wide range of statistical methods have been used, application of the statistical index (Wi) method has not been examined in flood susceptibility mapping. The aim of this research was to assess the efficiency of the Wi method and compare its outcomes with the results of frequency ratio (FR) and logistic regression (LR) methods. Thirteen factors, namely, altitude, slope, aspect, curvature, geology, soil, landuse/cover (LULC), topographic wetness index (TWI), stream power index (SPI), terrain roughness index (TRI), sediment transport index (STI), and distance from rivers and roads, were utilized. A flood inventory was constructed from data captured from the destructive flood that occurred in Brisbane, Australia, in 2011. Model performances were compared using the area under the curve (AUC), Kappa index and five other statistical evaluation tools. The AUC prediction rates acquired for LR, Wi and FR were 79.45%, 78.18%, and 67.33%, respectively. A more realistic representation of the flood-prone area distribution was produced by the Wi method compared to those of the other two techniques. Our research shows that the Wi method can be used as an efficient approach to perform flood susceptibility analysis.