  Urban flood risk emerges from complex and nonlinear interactions among
multiple features related to flood hazard, flood exposure, and social and
physical vulnerabilities, along with the complex spatial flood dependence
relationships. Existing approaches for characterizing urban flood risk,
however, are primarily based on flood plain maps, focusing on a limited number
of features, primarily hazard and exposure features, without consideration of
feature interactions or the dependence relationships among spatial areas. To
address this gap, this study presents an integrated urban flood-risk rating
model based on a novel unsupervised graph deep learning model (called
FloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence among
areas and complex and nonlinear interactions among flood hazards and urban
features for specifying emergent flood risk. Using data from multiple
metropolitan statistical areas (MSAs) in the United States, the model
characterizes their flood risk into six distinct city-specific levels. The
model is interpretable and enables feature analysis of areas within each
flood-risk level, allowing for the identification of the three archetypes
shaping the highest flood risk within each MSA. Flood risk is found to be
spatially distributed in a hierarchical structure within each MSA, where the
core city disproportionately bears the highest flood risk. Multiple cities are
found to have high overall flood-risk levels and low spatial inequality,
indicating limited options for balancing urban development and flood-risk
reduction. Relevant flood-risk reduction strategies are discussed considering
ways that the highest flood risk and uneven spatial distribution of flood risk
are formed.
