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Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network

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  • Yimin Lu

    (Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China
    Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China
    National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China)

  • Shiting Qiao

    (Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China
    Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China
    National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China)

  • Yiran Yao

    (Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China
    Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China
    National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China)

Abstract

Typhoon disasters not only trigger secondary disasters, such as rainstorms and flooding, but also bring many negative impacts on the normal operation of urban infrastructure and the safety of people’s lives and property. In order to effectively prevent the risks of typhoon disaster chain, this paper proposes a joint entity and relation extraction model based on RoBERTa-Adv-GPLinker. Then, relying on the ontology theory and methodology, we establish a knowledge graph of typhoon disaster chain. The results show that the joint extraction model based on RoBERTa-Adv-GPLinker outperforms other baseline models in all assessment indexes. Moreover, the constructed knowledge graph of typhoon disaster chain includes secondary disasters and derived disaster impacts. This can largely depict the evolution process of typhoon disaster secondary derivations. On this basis, a risk assessment model of typhoon disaster chain based on Bayesian network is established. Taking Fujian Province as an example, the risk associated with the typhoon disaster chain is assessed, verifying the effectiveness of the method. This study provides a scientific basis for enhancing government emergency response capabilities and achieving sustainable regional development.

Suggested Citation

  • Yimin Lu & Shiting Qiao & Yiran Yao, 2025. "Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network," Sustainability, MDPI, vol. 17(1), pages 1-27, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:331-:d:1560097
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    References listed on IDEAS

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    1. Jianxiu Wang & Xueying Gu & Tianrong Huang, 2013. "Using Bayesian networks in analyzing powerful earthquake disaster chains," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 509-527, September.
    2. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
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