Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information | |
Ge, Xingtong1,2; Yang, Yi3; Chen, Jiahui1,2; Li, Weichao1; Huang, Zhisheng4,5,6; Zhang, Wenyue1,2; Peng, Ling1 | |
发表期刊 | Remote Sensing |
2022 | |
期号 | 14页码:1214 |
摘要 | Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction. |
关键词 | disaster prediction knowledge graph spatio-temporal disaster dynamic prediction multi-source data fusion forest fire risk prediction geological landslide risk prediction |
收录类别 | SCIE |
语种 | 英语 |
WOS记录号 | WOS:000768742400001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47404 |
专题 | 数字内容技术与服务研究中心_智能技术与系统工程 |
通讯作者 | Peng, Ling |
作者单位 | 1.Aerospace Information Research Institute, Chinese Academy of Sciences 2.College of Resources and Environment (CRE), University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences 4.Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam 5.School of Computer Science and Engineering, Wuhan University of Science and Technology 6.Ztone International BV |
推荐引用方式 GB/T 7714 | Ge, Xingtong,Yang, Yi,Chen, Jiahui,et al. Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information[J]. Remote Sensing,2022(14):1214. |
APA | Ge, Xingtong.,Yang, Yi.,Chen, Jiahui.,Li, Weichao.,Huang, Zhisheng.,...&Peng, Ling.(2022).Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information.Remote Sensing(14),1214. |
MLA | Ge, Xingtong,et al."Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information".Remote Sensing .14(2022):1214. |
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