Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles

Taking into account the exponential growth of the number of flash-floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for flash-flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated. For this regard, 111 torrential points and 111 non-torrential points along with 8 flash-flood conditioning factors have been involved in the training process of the four models. The quality of the flash-flood models was checked by using the ROC Curve method, classification accuracy (CLA), and Kappa index. The result shows that the two ensemble models, the ADT-IOE (AUC = 0.972, CLC = 86.37%, Kappa statistics = 0.727) and the ADT-AHP (AUC = 0.926, CLA = 87.88%, Kappa statistics = 0.758), have high prediction performance and outperform the other models. Therefore, ADT-IOE and ADT-AHP are new and promising tools for flash-flood susceptibility modeling.