What question did this study set out to answer?

The central aim is to develop a robust framework for accurately quantifying flood-induced damage to buildings.

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

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Framework quantifies flood damage to buildings, suggesting a novel approach for disaster resilience in vulnerable areas.

The central aim is to develop a robust framework for accurately quantifying flood-induced damage to buildings.
Created a framework combining geospatial data, hydrodynamic modeling, and machine learning.
Applied random forest algorithms for building-type classification with high accuracy.
Utilized two-dimensional hydrodynamic simulations to assess inundation depths.
Conducted assessments during the 2023 flood event in Hebei Province, China.
Achieved 98.4% accuracy in classifying building types.
Identified maximum inundation depths ranging from 1.5 to 2.5 m.
Estimated structural damage ratios between 0.2–0.3 and interior damage ratios of 0.9–1.0.
Total direct economic damage to buildings reached approximately USD 1.42–1.69 billion.

Luo et al. (2026) studied this question.

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View Full Paper

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Authors

Framework quantifies flood damage to buildings, suggesting a novel approach for disaster resilience in vulnerable areas.

The central aim is to develop a robust framework for accurately quantifying flood-induced damage to buildings.
Created a framework combining geospatial data, hydrodynamic modeling, and machine learning.
Applied random forest algorithms for building-type classification with high accuracy.
Utilized two-dimensional hydrodynamic simulations to assess inundation depths.
Conducted assessments during the 2023 flood event in Hebei Province, China.
Achieved 98.4% accuracy in classifying building types.
Identified maximum inundation depths ranging from 1.5 to 2.5 m.
Estimated structural damage ratios between 0.2–0.3 and interior damage ratios of 0.9–1.0.
Total direct economic damage to buildings reached approximately USD 1.42–1.69 billion.

Luo et al. (2026) studied this question.

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