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：城市洪水风险源于与洪灾危险性、洪水暴露度、社会和物理脆弱性相关的多个要素之间复杂的非线性相互作用，以及复杂的空间洪水依赖关系。然而，现有刻画城市洪水风险的方法主要基于洪泛区地图，关注有限数量的要素（主要是危险性和暴露度特征），未考虑要素间相互作用或空间区域的依赖关系。为填补这一空白，本研究提出一种基于新型无监督图深度学习模型（称为FloodRisk-Net）的城市洪水风险等级综合评估模型。FloodRisk-Net能够捕捉区域间的空间依赖性，以及洪灾危险性与城市特征间复杂的非线性相互作用，以刻画涌现性洪水风险。利用美国多个大都市统计区（MSA）的数据，该模型将其洪水风险划分为六个鲜明的城市特定等级。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MSA中形成最高洪水风险的三种原型。研究发现，每个MSA内的洪水风险呈空间等级结构分布，核心城市不成比例地承担最高洪水风险。多个城市存在整体洪水风险水平高且空间不平等程度低的现象，表明在平衡城市发展与降低洪水风险之间选择有限。本文结合最高洪水风险形成方式及其空间分布不均衡性，讨论了相关的洪水风险减缓策略。