Rethinking Potential Flood Damage Assessment: A Data‐Driven Alternative to Varnes‐Based Models

ABSTRACT Reducing flood damage is a key challenge for both administrators and communities, particularly in the face of increasing climate‐related impacts. A fundamental prerequisite for effective damage reduction is a detailed understanding of the spatial distribution of territorial susceptibility to flood damage. In this context, we present an innovative, data‐driven methodology for mapping susceptibility to flood damage to private assets, leveraging machine learning techniques to overcome some of the limitations and uncertainties associated with traditional approaches based on the superposition of hazard, exposure, and vulnerability layers. The proposed methodology is applied to a case study in the Tuscany region of Italy, using approximately 11,000 claims related to flood events that occurred between 2013 and 2023. The claims are analyzed in relation to 15 predisposing factors representing both territorial and socio‐environmental characteristics of the study area. The introduction of negative samples, corresponding to locations where residential damage is assumed to be negligible or absent, allows the model to distinguish susceptible from non‐susceptible areas. The susceptible areas are subsequently classified into damage magnitude classes based on citizens' self‐reported assessments. The intrinsic variability and non‐linearity of the process, together with the uncertainty associated with citizen‐reported data and potential perception and reporting biases, are addressed through a second‐stage ensemble of independently trained models. The final outputs include a majority‐vote susceptibility map and a map of prediction stability. The model performs well in identifying the occurrence of reported damage, while the discrimination between damage magnitude classes remains more challenging, highlighting the intrinsic uncertainty associated with citizen‐reported damage data. The resulting maps are intended as practical tools to support flood damage reduction efforts and should be viewed as complementary to existing flood risk maps used in land‐use planning. The proposed framework provides a transferable approach for using observed damage records to characterize spatial patterns of potential damage, while its application to new geographical contexts requires site‐specific calibration and validation.