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

Floods have become more and more severe and frequent with global climate change. The present study focuses on the Black Sea’s immediate riparian area over which the Danube Delta extends. Due to the accelerated increase in the severity of floods, the vulnerability of the deltaic areas is augmenting. Therefore, it is very important to adopt measures to mitigate the negative effects of these phenomena. The basis of the measures to limit the negative effects is the activity of identifying areas prone to flooding. Thus, this research paper presents a methodology for estimating flood susceptibility using the Analytical Hierarchy Process (AHP) and Fuzzy-Analytical Hierarchy Process (FAHP) models. To determine the susceptibility to these natural risk phenomena, the following eight flood predictors were taken into account: slope, elevation, altitude above channel, land use, hydrological soil group, lithology distance from the river, and distance from water bodies. Furthermore, the weights that each flood predictor has in terms of determining flood susceptibility were determined through the previously mentioned models. The results revealed that the slope is the most important predictor, followed by elevation, distance from the river, and land use. These weights were used in the GIS environment to evaluate the susceptibility to floods from a spatial point of view. The areas with a high/very high value for these phenomena occupy over 70% of the surface of the Danube Delta.

It is clear that flash flood events are on the rise, and the detection of flash flood-prone areas has now become a top priority for governments throughout the world. Flash-flood potential mapping can provide very useful information that can be used in order to adopt the most appropriate measures to reduce the risk of these phenomena. A precise model is needed in order to predict flash flood potential, which can help to improve flood management strategies, as it can be used to develop more effective flood management methods. In this present research, Index of Entropy (IOE), Logistic Regression (LR), four machine learning models (, Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), k-Nearest Neighbour (k-NN)) and their 5 stacking ensembles were used to construct a new GIS-based framework for flash flood-potential mapping in the Suha River Basin, Romania. For the purposes of running the models, 8 topographical and environmental factors (slope, land use, plan curvature, profile curvature, convergence index, aspect, hydrological soil groups and tpi), along with 111 torrential locations were used to run the five applied models. In order to facilitate training and validation purposes, the entire dataset was divided into 70:30 parts. ROC Curve and statistical measures were employed in order to verify the model reliability and also validate the results. It is noteworthy here that in the training procedure, the stacking ensemble constructed through the combination of all machine learning and statistical learning models (LCkNS) was better than all the other models with a maximum accuracy of 0.972. The models used, revealed that between 10% and 22% of Suha river basin is occupied by high and very high flash-flood potential. As a result, the data generated by this research provides reliable results that can assist policymakers at the local and national levels in developing a concrete strategic plan to reduce flood frequency in their area by utilizing a flood warning system.

Floods, along with other natural and anthropogenic disasters, profoundly disrupt both society and the environment. Populations residing in deltaic regions worldwide are particularly vulnerable to these threats. A prime example is the Danube Delta (DD), located in the Romanian sector of the Black Sea. This research paper aims to identify areas within the DD that are highly or very highly susceptible to flooding. To accomplish this, we employed a combination of multicriteria decision-making (AHP) and artificial intelligence (AI) techniques, including deep learning neural networks (DLNNs), support vector machines (SVMs), and multilayer perceptron (MLP). The input data comprised previously flooded regions alongside eight geographical factors. All models identified high or very high flood potential of over 65% of the studied area. The models’ performance was assessed using receiver operating characteristic (ROC) analysis, demonstrating excellent outcomes evaluated by the area under the curve (AUC) exceeding 0.908. This study is significant as it lays the groundwork for implementing measures against flood impacts in the DD.