Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models

Abstract

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.