Spatial prediction of flood-prone areas in the Calabria Region (Southern Italy) using historical flood inventories and Maximum Entropy approach

Floods are the most frequent natural hazard worldwide, causing severe impacts on human populations and leading to substantial economic and environmental losses. In recent decades, the frequency and intensity of flood events have increased significantly, largely due to the growing occurrence of extreme climatic phenomena. Consequently, flood susceptibility mapping has become a crucial tool for flood hazard assessment, management, and mitigation. This study aims to predict and map flood-prone areas in Calabria region (southern Italy) by integrating historical flood data, Geographic Information Systems (GIS), and the Maximum Entropy (ME) modeling approach. A catalogue of flood events impact recorded in the study region between 2000 and 2025 by documentary sources was systematically analyzed within a GIS environment. A total of 270 flood occurrence points affected by flood damage were identified and mapped; 70% of these were randomly selected to construct a balanced training dataset and calibrate the prediction model, while the remaining 30% were used for validation. For the application of the ME method, the flood inventory was combined with fifteen flood-predisposing factors, including lithology, soil texture, land use, normalized difference vegetation index (NDVI), precipitation, elevation, local relief (LR), slope, curvature, topographic position index (TPI), sediment transport index (STI), topographic wetness index (TWI), drainage density (DD), distance to streams, and distance to roads. The validation of the flood-prone areas model was performed based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and its associated area under the curve (AUC). The results indicate very good predictive performance of the model, with success and prediction rates of 89.7% and 86.3%, respectively. In addition, the jackknife test highlighted the significant contribution of soil texture, TWI, precipitation, distance to streams, and land use to the spatial prediction of flood occurrence. The produced flood-prone areas map provides a valuable tool for disaster risk management and mitigation planning, offering significant support to decision-makers in reducing both economic losses and flood-related risks to human life.