Geo-ontology model construction for urban flood disaster: a case study of community scale

Urban flooding has become one of the most common natural disasters worldwide due to recent global climate changes. Therefore, disaster simulation and forecasting have become important “non-engineering measures” to manage urban flooding and reduce the effective emergency response time. In this study, an urban flooding-disaster geographic-ontology model (UFD-GOM) was constructed with rainfall, surface, and drainage ontologies as its core, and a Bayesian network model was built after the classes, relationships, and attributes were rationally defined. The Caochangmen Community, Nanjing, China, case-study results showed that the difference between the number of inferred/predicted and actual observed disaster-class grid cells was small. The spatial distribution of the flood-prone monitoring points and the inferred/predicted regional disaster-occurrence intensity were well matched, with the average accuracy being 87.18%, verifying and validating the scientific basis and practicality of the “triple-core” UFD-GOM constructed in this study.