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A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide Scenes

Author

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  • Juan Li

    (College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Institute of Surveying, Mapping and Geo-Information, Taiyuan 030001, China)

  • Jin Zhang

    (College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Li Wang

    (College of Computer Science and Technology (Data Science), Taiyuan University of Technology, Taiyuan 030600, China)

  • Ao Zhao

    (College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale spatiotemporal process within landslide scenes. To address these issues, we proposed a hierarchical spatiotemporal data model, named as HSDM, to enhance the representation for geographic scenes. Specifically, we introduced a spatiotemporal object model that integrates both their structural and process information of objects. Furthermore, we extended the process definition to capture complex spatiotemporal processes. We sorted out the relationships used in HSDM and defined four types of spatiotemporal correlation relations to represent the connections between spatiotemporal objects. Meanwhile, we constructed a three-level graph model of geographic scenes based on these concepts and relationships. Finally, we achieved representation and modeling of a dynamic landslide scene in Heifangtai using HSDM and implemented complex querying and reasoning with Neo4j’s Cypher language. The experimental results demonstrate our model’s capabilities in modeling and reasoning about complex multi-scale information and spatio-temporal processes with landslide scenes. Our work contributes to landslide knowledge representation, inventory and dynamic simulation.

Suggested Citation

  • Juan Li & Jin Zhang & Li Wang & Ao Zhao, 2024. "A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide Scenes," Sustainability, MDPI, vol. 16(23), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10271-:d:1527975
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    References listed on IDEAS

    as
    1. Wenhao Wan & Yongzhong Tian & Jinglian Tian & Chengxi Yuan & Yan Cao & Kangning Liu, 2024. "Research Progress in Spatiotemporal Dynamic Simulation of LUCC," Sustainability, MDPI, vol. 16(18), pages 1-18, September.
    2. Sérgio C. Oliveira & José L. Zêzere & Ricardo A. C. Garcia & Susana Pereira & Teresa Vaz & Raquel Melo, 2024. "Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations," 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. 120(10), pages 9361-9399, August.
    3. Zhuoyu Lv & Shanshan Wang & Shuhao Yan & Jianyun Han & Gaoqiang Zhang, 2024. "Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling," Sustainability, MDPI, vol. 16(19), pages 1-20, September.
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