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Floods are among the most severe natural hazard phenomena that affect people around the world. Due to this fact, the identification of zones highly susceptible to floods became a very important activity in the researcher’s work. In this context, the present research work aimed to propose the following 3 novel ensembles to estimate the flood susceptibility in Putna river basin from Romania: UltraBoost-Weights of Evidence (U-WOE), Stochastic Gradient Descending-Weights of Evidence (SGD-WOE) and Cost Sensitive Forest-Weights of Evidence (CSForest-WOE). In this regard, a sample of 132 flood locations and 14 flood predictors was used as input datasets in the 3 aforementioned models. The modeling procedure performed through a ten-fold cross-validation method revealed that the SGD-WOE ensemble model achieved the highest performance in terms of ROC Curve-AUC (0.953) and also in terms of Accuracy (0.94). The slope and distance from river flood predictors achieved the highest importance in terms of flood susceptibility genesis, while the aspect, TPI, hydrological soil groups, and plan curvature have the lowest influence in terms of flood occurrence. The area with high and very high susceptibility represents between 21% and 24% of the Putna river basin from Romania.

Floods represent the natural hazards that generate the most damage at the international level. A very important stage in the flood risk management activity is the mapping of areas susceptible to these hazards. In this context, in the present study, the following 3 hybrid models were applied to determine flood susceptibility in Putna river basin, Romania: Randon Committee-Weights of Evidence (RC-WOE), Random SubSpace-Weights of Evidence (RSS-WOE) and Randomizable Filtered Classifier - Weights of Evidence (RFC - WOE). 14 flood predictors and 192 flood locations (divided into 70% training sample and 30% validating sample) were used as input data in the 3 models. The applied models confirmed the fact that the most important flood predictors are: slope angle, distance from rivers and elevation. At the same time, around 24% of the study area shows a high and very high susceptibility to floods. The ROC Curve method along with other statistical metrics, used to validate the applied models, showed that the accuracy of the models generally exceeded 80%, which represents a very good performance. The obtained results provide useful information for the authorities responsible for reducing the flood risk. Also, the future planning of the territory can obviously take into account the zoning of flood susceptibility.