Riverine flood potential assessment using metaheuristic hybrid machine learning algorithms

Abstract

This study presents the performance of stand-alone and novel hybrid models combining the feed-forward neural network (FFNN) and extreme gradient boosting (XGB) with the genetic algorithm (GA) optimization to determine the riverine flood potential at a local spatial scale, which is represented by the Gidra river basin, Slovakia. Eleven flood factors and a robust flood inventory database, consisting of 10,000 flood and non-flood locations, were used. Using the FFNN, XGB, GA-FFNN and GA-XGB models, 16.5%, 11.0%, 17.1%, and 12.3% of the studied basin, respectively, is characterized with high to very high riverine flood potential. The applied models resulted in very high accuracy, that is, AUC = 0.93 in case of the FFNN stand-alone model and AUC = 0.96 in case of the XGB stand-alone model. The GA algorithm was able to raise the value of AUC for the hybrid GA-FFNN and GA-XGB models to 0.94 and 0.97, respectively. The results of this study can be useful, especially, for the identification of the areas with the highest potential for riverine floods within the next updating of the Preliminary Flood Risk Assessment, which is being carried out based on the EU Floods Directive.