From Expert-Based Evaluation to Data-Driven Modeling: Performance-Based Flood Susceptibility Mapping

Floods are natural disasters that cause significant socioeconomic and environmental losses in both urban and rural areas. Within the framework of spatial planning, precautionary measures against flood hazards can be developed using analytical approaches based on different modeling techniques. In this study, flood-prone areas in the Melen Basin, Türkiye, were identified and mapped using five statistical methods, namely Frequency Ratio (FR), Shannon Entropy (SE), Evidential Belief Function (EBF), and the hybrid models EBF–SE and EBF–FR. The analysis was conducted using a flood inventory and environmental datasets covering the period 2019–2024, including elevation, slope, aspect, land use, plan and profile curvature, drainage density, distance to river, curve number, long-term average precipitation, geological formation, soil depth, topographic wetness index, sediment transport, and stream power index. Model performances were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). The results indicate that the SE method achieved the highest predictive performance (AUC = 0.979), followed by FR (0.974), EBF–SE (0.972), EBF–FR (0.968), and EBF (0.966). According to the FR and SE models, elevation, lithology, and slope were identified as the most influential factors in flood occurrence. In the evaluation of the success index of the models, the following values were determined according to their size: EBF–SE (96.0), SE (94.4), EBF (91.8), FR (81.9), and EBF–FR (79.4). In the classification of flood sensitivity maps, Natural Breaks (Jenks) is the most successful method according to the success index. The findings demonstrate that data-driven and hybrid models can effectively support flood risk assessment and provide valuable input for land-use planning and flood risk management.