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.

翻译：理解塑造城市区域固有洪灾风险倾向的基本特征对于制定综合性的洪灾风险减缓城市设计策略至关重要。洪灾风险倾向定义了与事件无关的固有财产洪灾风险强度，并衡量城市区域在遭遇气象灾害时遭受财产损失的敏感程度。本研究提出洪泛基因组（FloodGenome）作为一种可解释机器学习模型，用于评估各类水文、地形及建成环境特征及其交互作用如何塑造城市区域的洪灾风险倾向。基于美国国家洪水保险计划2003年至2023年间覆盖四个大都市统计区（MSA）的洪灾损失索赔数据，本研究通过k-means聚类分析计算建筑损失比率与洪水索赔数量，将人口普查区块组（CBG）划分为不同的财产洪灾风险倾向等级。随后，基于水文、地形及建成环境等多重交织特征，构建随机森林模型以预测CBG的财产洪灾风险等级。模型迁移性分析结果显示各MSA间性能一致，揭示了塑造城市财产洪灾风险的底层特征具有普适性。洪泛基因组模型被应用于：（1）评估未来城市发展对区域洪灾风险倾向的加剧程度；（2）以更精细空间分辨率指定财产洪灾风险等级，为洪灾管理流程提供关键见解。该模型及其发现为改善对塑造城市洪灾风险特征的多重交互特征的刻画与理解，提供了新颖工具与理论洞见。