Intelligent Methods for Estimating the Flood Susceptibility in the Danube Delta, Romania

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.