Yu Wang
ABSTRACT
Increasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage. Researchers have been dedicating to urban flood risk assessments in recent years. However, current research is still facing the challenges of multi-modal data fusion and knowledge representation of urban flood events. Therefore, in this paper, we propose an Urban Flood Knowledge Graph (UrbanFloodKG) system that enables KG to support urban flood risk assessment. The system consists of data layer, graph layer, algorithm layer, and application layer, which implements knowledge extraction and storage functions, integrates knowledge representation learning models and graph neural network models to support link prediction and node classification tasks. We conduct model comparison experiments on link prediction and node classification tasks based on urban flood event data from Guangzhou, and demonstrate the effectiveness of the models used. Our experiments prove that the accuracy of risk assessment can reach 91% when using GEN, which provides a a promising research direction for urban flood risk assessment.
Supplemental Material
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Index Terms
- UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment
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