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.

翻译：城市洪水风险源于洪水灾害、洪水暴露、社会和物理脆弱性等多重特征之间的复杂非线性交互作用，以及复杂的空间洪水依赖关系。然而，现有描述城市洪水风险的方法主要基于洪泛区地图，侧重于有限数量的特征（主要是灾害和暴露特征），未考虑特征交互或空间区域之间的依赖关系。为填补这一空白，本研究提出了一种基于新型无监督图深度学习模型（称为FloodRisk-Net）的综合城市洪水风险等级模型。FloodRisk-Net能够捕捉区域间的空间依赖关系以及洪水灾害与城市特征之间的复杂非线性交互作用，从而明确新兴洪水风险。利用美国多个大都市统计区的数据，该模型将其洪水风险划分为六个独特的城市特定等级。该模型具有可解释性，能够对每个洪水风险等级内的区域进行特征分析，从而识别出每个大都市统计区内塑造最高洪水风险的三种原型模式。研究发现，在每个大都市统计区内，洪水风险呈分层结构空间分布，核心城区不成比例地承担最高洪水风险。多个城市呈现出高总体洪水风险水平和低空间不平等性，表明城市发展与洪水风险减缓之间的平衡选择有限。针对最高洪水风险及洪水风险空间分布不均的形成机制，本文讨论了相关的洪水风险减缓策略。