Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models

An accurate assessment of Flash-Flood Potential for certain areas is mandatory for the improvement of flash-flood forecast and warnings. The main aim of the present study is represented by the calculation of Flash-Flood Potential Index within the upper and the middle sector of Prahova river catchment (Romania) by using 4 hybrid models: Logistic Regression-Frequency Ratio (LR-FR) model, Logistic Regression-Weights of Evidence (LR-WoE) model, Support Vector Machine-Frequency Ratio (SVM-FR) model and Support Vector Machine-Weights of Evidence (SVM-WoE). The identification of areas affected by torrential phenomena represents the first step performed in the present research. These areas with a total surface of 260 km2 were divided into training areas (70%) and validating areas (30%). By the mean of Linear Support Vector Machine (LSVM) model, 10 flash-flood conditioning factors were selected and further used for the Flash-Flood Potential assessment. Based on the spatial relationship between areas affected by torrential phenomena and flash-floods conditioning factors characteristics, the FR and WoE coefficients were calculated. In order to be integrated into Logistic Regression and Support Vector Machine (RBF) analysis, these values were standardized. According to the results of the 4 hybrid models used for FFPI calculation, the high and very high Flash-Flood Potential are spread over 33% of the study area. The model performance assessment and results validation were carried out by the mean of the three different methods: i) relative frequency distribution of torrential phenomena pixels within FFPI classes; ii) ROC Curve (Success Rate and Prediction Rate) and AUC value; iii) statistical measures represented by Sensitivity, Specificity and Accuracy. [Display omitted] •One of the first attempts to calculate Flash-Flood Potential using hybrid models•Areas affected by torrential phenomena were considered into the analysis.•10 flash-flood conditioning factors were selected based on statistical analysis.•Areas with high flash-flood susceptibility were identified.•By using three methods the results' validation was carried out.