The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Zhang, Yunhao; Zhu, Jun; Zhu, Qing; Xie, Yakun; Li, Weilian; Fu, Lin; Zhang, Junxiao; Tan, Jianmei
• Is Part Of: International journal of digital earth. Volume 13:Issue 12(2020)
• Page Start: 1637
• Page End: 1655
• Publication Date: 2020-12-01
• Subjects: Landslide disaster scene -- virtual disaster environment -- knowledge graph -- deep neural network -- personalized recommendation; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2020.1773950
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Virtual Landslide Disaster environments are important for multilevel simulation, analysis and decision-making about Landslide Disasters. However, in the existing related studies, complex disaster scene objects and relationships are not deeply analyzed, and the scene contents are fixed, which is not conducive to meeting multilevel visualization task requirements for diverse users. To resolve the above issues, a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper. The characteristics of relationships among users, scenes and data were first discussed in detail; then, a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects, and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph. Therefore, a personalized Landslide Disaster scene data recommendation mechanism was proposed. Finally, a prototype system was developed, and an experimental analysis was conducted. The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users. The recommendation accuracy stabilizes above 80% – a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.