Flood loss estimation using 3D city models and remote sensing data. (July 2018)

• Record Type: Journal Article
• Authors: Schröter, Kai; Lüdtke, Stefan; Redweik, Richard; Meier, Jessica; Bochow, Mathias; Ross, Lutz; Nagel, Claus; Kreibich, Heidi
• Is Part Of: Environmental modelling & software. Volume 105(2018)
• Page Start: 118
• Page End: 131
• Publication Date: 2018-07
• Subjects: Flood risk -- Flood loss modeling -- Standardized data -- Random forests -- Vulnerability -- Virtual 3D city models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.032
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Flood loss modeling provides the basis to optimize investments for flood risk management. However, detailed object-related data are not readily available to generate spatially explicit risk information. Virtual 3D city models and numerical spatial measures derived from remote sensing data provide standardized data and hold promise to fill this gap. The suitability of these data sources to characterize the vulnerability of residential buildings to flooding is investigated using the city of Dresden as a case study, where also empirical data on relative flood loss and inundation depths are available. Random forests are used for predictive analysis of these heterogeneous data sets. Results show that variables depicting building geometric properties are suitable to explain flood vulnerability. Model validation confirms that predictive accuracy and reliability are comparable to alternative models based on detailed empirical data. Furthermore, virtual 3D city models allow embedding vulnerability information into flood risk sensitive urban planning. Highlights: Flood loss models for residential buildings are developed based on 3D city models and remote sensing data. These multi-variable predictive models are validated using empirical data. 3D city models are readily available for urban areas and as standardized data they ease the spatial transfer of loss models. Building vulnerability information is embedded into virtual 3D city models to support flood risk sensitive urban planning.