A Similarity-Based Fuzzy Framework for Flood Damage Assessment Under Data-Scarce Conditions

The assessment of building-level flood damage in low-relief floodplains is constrained by pronounced exposure heterogeneity and a lack of object-level damage data. This study proposes a similarity-based fuzzy modeling framework for direct material flood damage assessment under structurally data-scarce conditions. The approach combines a Composite Exposure Index derived from geospatial indicators with a Mamdani-type fuzzy inference system and a prototype-based similarity modulation mechanism that enhances differentiation among highly exposed buildings without empirical calibration. The framework was evaluated using a physically consistent synthetic dataset representing a rural lowland floodplain in Serbia. The results demonstrate smooth and monotone damage escalation with respect to exposure and flood depth, while similarity-based modulation selectively enhances discriminatory resolution in high-exposure regimes. The proposed framework provides a transparent and data-efficient alternative to calibration-dependent empirical and machine-learning approaches for exploratory flood-risk analysis and decision-support applications.