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An on-demand construction method of disaster scenes for multilevel users

Author

Listed:
  • Weilian Li

    (Southwest Jiaotong University)

  • Jun Zhu

    (Southwest Jiaotong University)

  • Yunhao Zhang

    (Southwest Jiaotong University)

  • Lin Fu

    (Southwest Jiaotong University)

  • Yuhang Gong

    (The Chinese University of Hong Kong)

  • Ya Hu

    (Southwest Jiaotong University)

  • Yungang Cao

    (Southwest Jiaotong University)

Abstract

Disaster scenes can effectively transmit disaster information and help people make sensible decisions. However, the current 3D scenes of disasters still have certain limitations. First, related studies have focused on the construction of 3D scene technology itself and lacked a detailed semantic description of the disaster scene, which is not conducive to standardizing the process of scene construction and supporting efficient analysis. Second, the 3D scene is generally fixed, preventing full consideration of the different needs of multilevel users involved in disaster management. This paper proposes an on-demand construction method of disaster scenes for multilevel users. The creation of a knowledge graph for disasters, calculation of semantic relevance and optimal selection of scene contents are discussed in detail. Finally, taking a debris flow disaster as an example, a prototype system is developed to implement experimental analysis. The experimental results show that the constructed knowledge graph can normalize the semantic relationships among multilevel users, scene objects and visualization methods in a formal way and accurately describe the different needs of multilevel users. The 3D scenes of debris flow disasters driven by the knowledge graph can reduce the complexity and difficulty of the modeling process while satisfying the diverse needs of multilevel users.

Suggested Citation

  • Weilian Li & Jun Zhu & Yunhao Zhang & Lin Fu & Yuhang Gong & Ya Hu & Yungang Cao, 2020. "An on-demand construction method of disaster scenes for multilevel users," 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. 101(2), pages 409-428, March.
    • Handle: RePEc:spr:nathaz:v:101:y:2020:i:2:d:10.1007_s11069-020-03879-z
    DOI: 10.1007/s11069-020-03879-z
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    References listed on IDEAS

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    1. Guoqiang Peng & Yongning Wen & Yuting Li & Songshan Yue & Zhiyao Song, 2018. "Construction of collaborative mapping engine for dynamic disaster and emergency response," 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. 90(1), pages 217-236, January.
    2. Shabana Khan & Jyoti L. Mishra & Kuna-hui Elaine Lin & Emma E. H. Doyle, 2017. "Rethinking communication in risk interpretation and action," 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. 88(3), pages 1709-1726, September.
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    4. Andrea D’Aniello & Luca Cozzolino & Luigi Cimorelli & Renata Della Morte & Domenico Pianese, 2015. "A numerical model for the simulation of debris flow triggering, propagation and arrest," 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. 75(2), pages 1403-1433, January.
    5. D. Bhatt & R. Mall & T. Banerjee, 2015. "Climate change, climate extremes and disaster risk reduction," 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. 78(1), pages 775-778, August.
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