Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques

Abstract

Abstract River flooding can be a highly destructive natural hazard. Numerous approaches have been used to study the phenomenon; however, insufficient knowledge regarding flood conditioning factors continues to hinder prevention and control measures. This research examines the hypothesis that by adding further conditioning factors to a dataset used in river flood modeling, increases the accuracy of the final susceptibility mapping result. Additionally, this study assesses the impact of individual conditioning factors on flood susceptibility mapping and their importance in the construction of precise mapping of potential flood regions. Two robust machine learning approaches, Decision Tree (DT) and Support Vector Machine (SVM), were utilized to evaluate spatial correlations between flood conditioning factors and rate their level of importance for mapping the flood prone areas. For this purpose, two datasets were used; dataset 1 (DS1): Light Detection and Ranging (LiDAR) derived factors of altitude, slope, aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Topographic Roughness Index (TRI), and Sediment Transport Index (STI) and dataset 2 (DS2): a combination of LiDAR derived factors supplemented by geology, soil, landuse/cover (LULC), distance from roads and distance from rivers parameters. An extreme flood event in 2011 in Brisbane, Australia was used as a case study, in which DT and SVM techniques were both applied, using both datasets. In addition, multi-collinearity, variance inflation factors (VIF), Pearson's correlation coefficients and Cohen's kappa analysis provided useful information regarding the inter-relationships of factors, as well as the influence of each factor on the precision of the final map. The area under curve (AUC) method was used for accuracy assessment. SVM and DT produced the highest accuracies of prediction, with rates of 85.52% and 88.47% respectively, using DS1 (the LiDAR dataset). Altitude, SPI and TRI were found to have a significant impact on the precision of the outcomes. It was concluded that the inclusion of additional factors in the modeling, does not necessarily guarantee the achievement of greater accuracy. However, the modeling method, can significantly alter outcomes.