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.

虚拟滑坡灾害环境对于滑坡灾害的多层次仿真，分析和决策至关重要。但是，在现有的相关研究中，对复杂的灾难场景对象和关系没有进行深入的分析，并且场景的内容是固定的，不利于满足不同用户的多层次可视化任务要求。针对上述问题，提出了一种基于知识图和深度神经网络的个性化虚拟滑坡灾害环境构建方法。首先详细讨论了用户，场景和数据之间的关系特征。然后，建立虚拟滑坡灾害环境的知识图，以阐明灾害现场对象之间的复杂关系，并引入了深度神经网络来挖掘用户历史信息以及知识图中对象实体之间的关系。因此，提出了一种个性化的滑坡灾害现场数据推荐机制。最后，开发了原型系统，并进行了实验分析。实验结果表明，该方法可用于向各种用户智能地推荐适当的灾害信息和现场数据。建议的准确性稳定在80％以上，该水平能够有效地支持“个性化虚拟滑坡灾害”环境的构建。开发了原型系统，并进行了实验分析。实验结果表明，该方法可用于向各种用户智能地推荐适当的灾害信息和现场数据。建议的准确性稳定在80％以上，该水平能够有效地支持“个性化虚拟滑坡灾害”环境的构建。开发了原型系统，并进行了实验分析。实验结果表明，该方法可用于向各种用户智能地推荐适当的灾害信息和现场数据。建议的准确性稳定在80％以上，该水平能够有效地支持“个性化虚拟滑坡灾害”环境的构建。