Knowledge Graph Representation Learning-Based Forest Fire Prediction
文献类型：期刊论文
| 作者 | Chen, Jiahui1,3; Yang, Yi2 ; Peng, Ling1,3; Chen, Luanjie1,3; Ge, Xingtong1,3
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 14期号:17页码:19 |
| 关键词 | forest fire graph neural network disaster prediction knowledge graph link prediction |
| DOI | 10.3390/rs14174391 |
| 通讯作者 | Peng, Ling(pengling@aircas.ac.cn) |
| 英文摘要 | Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-source data fusion. Therefore, the forest fire predictions ignore the complex dependencies and correlations of the spatiotemporal kind that usually bring valuable information for the predictions. Although the knowledge graph methods have been used to model the forest fires data, they mainly rely on artificially defined inference rules to make predictions. There is currently a lack of a representation and reasoning methods for forest fire knowledge graphs. We propose a knowledge-graph- and representation-learning-based forest fire prediction method in this paper for addressing the issues. First, we designed a schema for the forest fire knowledge graph to fuse multi-source data, including time, space, and influencing factors. Then, we propose a method, RotateS2F, to learn vector-based knowledge graph representations of the forest fires. We finally leverage a link prediction algorithm to predict the forest fire burning area. We performed an experiment on the Montesinho Natural Park forest fire dataset, which contains 517 fires. The results show that our method reduces mean absolute deviation by 28.61% and root-mean-square error by 53.62% compared with the previous methods. |
| WOS关键词 | RISK |
| 资助项目 | Ningxia Key RD Program[2020BFG02013] ; Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis[2021-04] ; Beijing Municipal Science and Technology Project[Z191100001419002] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000851954200001 |
| 出版者 | MDPI |
| 资助机构 | Ningxia Key RD Program ; Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis ; Beijing Municipal Science and Technology Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50110]  |
| 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.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Jiahui,Yang, Yi,Peng, Ling,et al. Knowledge Graph Representation Learning-Based Forest Fire Prediction[J]. REMOTE SENSING,2022,14(17):19. |
| APA | Chen, Jiahui,Yang, Yi,Peng, Ling,Chen, Luanjie,&Ge, Xingtong.(2022).Knowledge Graph Representation Learning-Based Forest Fire Prediction.REMOTE SENSING,14(17),19. |
| MLA | Chen, Jiahui,et al."Knowledge Graph Representation Learning-Based Forest Fire Prediction".REMOTE SENSING 14.17(2022):19. |
入库方式： OAI收割
来源：自动化研究所
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