Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning. Issue 25 (13th December 2022) - Costache, Romulus; Pham, Quoc Bao; Arabameri, Alireza; Diaconu, Daniel Constantin; Costache, Iulia; Crăciun, Anca; Ciobotaru, Nicu; Pandey, Manish; Arora, Aman; Ali, Sk Ajim; Pham, Binh Thai; Nguyen, Hoang; et al.
Abstract: The present study aims to enrich the specialized literature by proposing and calculating a new flash-flood propagation susceptibility index (FFPSI). Thus, firstly the Flash-Flood Potential Index (FFPI) using the ensembles of the next models was calculated: Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Logistic Regression (LR), Classification and Regression Trees (CART), and Radial Basis Function Neural Network-Weights of Evidence (RBFN-WOE). A number of 255 flash-flood locations, split into training (70%) and validating (30%) samples, along with 10 predictors were used as input in the five models. The Receiver Operating Characteristics (ROC) Curve and several statistical metrics were used to evaluate the Flash-Flood Potential Index results. LR-WOE and AHP-WOE were the most performant models. Nevertheless, all the applied models performed very well (AUC > 0.85). Further, the FFPSI was determined by integrating the FFPI results into a Flow Accumulation procedure. Over 55% of the valleys identified are characterized by high and very high values of FFPSI. Graphical abstract: UF0001