Development of a building-scale integrated flood damage quantifying framework using a hydrodynamic model and multisource geospatial data

Building damage is the primary component of economic damage resulting from flood disasters. Understanding flood damage enables effective disaster risk reduction strategies and community resilience planning. In this study, a comprehensive framework for quantifying flood-induced damage to individual building properties (structural and content) is developed. This methodology combines geospatial data with machine learning and hydrodynamic modeling, as demonstrated through the 2023 flood event in the Dongdian flood storage and detention area (FSDA), Hebei Province, China. The main findings are as follows: (1) building-type classification using random forest algorithms achieved 98.4% accuracy in distinguishing residential, commercial, and industrial structures; (2) two-dimensional hydrodynamic simulations revealed maximum inundation depths predominantly ranging from 1.5 to 2.5 m, with structural damage ratios of 0.2–0.3 and interior property damage ratios of 0.9–1.0; (3) total direct economic damage to building properties in the Dongdian FSDA reached CNY 10.00–11.91 billion (approximately USD 1.42–1.69 billion), with industrial buildings accounting for 68.74% of damage, representing the dominant damage category. This framework delivers a precise flood damage assessment of building properties, transcends traditional survey limitations and offers a globally transferable approach for enhancing disaster resilience and reducing property risks in flood-vulnerable regions, subject to appropriate data availability and parameter adaptation.