Urban flash flood prediction modelling using probabilistic and statistical approaches

The development of a detailed strategy to mitigate the negative consequences of any natural calamities depends on accurately identifying sensitive zones where natural hazards frequently happen. In the present investigations, three widely utilized probabilistic approaches viz., frequency ratio (FR), statistical index (SI), and weighting factor (WF) have been utilized for prediction of flsh flood susceptibility zones in the Coochbehar urban and peri-urban area (CUPUA) (area = 26.22 km2). Ten flash flood conditioning factors have been used in this assessment based on previous literatures and experts' opinions. In the FR model, 29.40 % area is observed in the high and very high flood zones, whereas 36.27 % and 31.16 % area is identified in SI and WF model, respectively. The FR model demonstrates that five conditioning factors, viz., topographic position index (TPI), land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to drainage (DtD) and rainfall were highly impacted in flash flood prediction (FFP) analysis; in SI model, LULC is the major influencing parameter, and in WF model LULC, rainfall, NDVI, and distance to road (DtR) are the effective parameters. The success rate curve of the FR, SI and WF models manifest SI model has highest training (AUC=0.903) and prediction (AUC=0.925) accuracy, and FR and WF also have very good accuracy as their AUC values are 0.899 and 0.877 (in success rate curve) and 0.900 and 0.881 (in prediction rate curve). Therefore, the application of probabilistic approaches in this active flash flood-prone region is excellently performed, and the results of this study will help hydrologists, engineers, and water management administrators to control the areas that are extremely susceptible to flash floods and reduce possible damages.