  Understanding the fundamental characteristics that shape the inherent flood
risk disposition of urban areas is critical for integrated urban design
strategies for flood risk reduction. Flood risk disposition specifies an
inherent and event-independent magnitude of property flood risk and measures
the extent to which urban areas are susceptible to property damage if exposed
to a weather hazard. This study presents FloodGenome as an interpretable
machine learning model for evaluation of the extent to which various
hydrological, topographic, and built-environment features and their
interactions shape flood risk disposition in urban areas. Using flood damage
claims data from the U.S. National Flood Insurance Program covering the period
2003 through 2023 across four metropolitan statistical areas (MSAs), the
analysis computes building damage ratios and flood claim counts by employing
k-means clustering for classifying census block groups (CBGs) into distinct
property flood risk disposition levels. Then a random forest model is created
to specify property flood risk levels of CBGs based on various intertwined
hydrological, topographic, and built-environment features. The model
transferability analysis results show consistent performance across MSAs,
revealing the universality of underlying features that shape city property
flood risks. The FloodGenome model is then used to:(1) evaluate the extent to
which future urban development would exacerbate flood risk disposition of urban
areas; and (2) specify property flood risk levels at finer spatial resolution
providing critical insights for flood risk management processes. The
FloodGenome model and the findings provide novel tools and insights for
improving the characterization and understanding of intertwined features that
shape flood risk profiles of cities.
