Knowledge Graph Representation Learning-Based Forest Fire Prediction
Chen, Jiahui1,2; Yang, Yi3; Peng, Ling1,2; Chen, Luanjie1,2; Ge, Xingtong1,2
发表期刊REMOTE SENSING
2022-09-01
卷号14期号:17页码:19
通讯作者Peng, Ling(pengling@aircas.ac.cn)
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.
关键词forest fire graph neural network disaster prediction knowledge graph link prediction
DOI10.3390/rs14174391
关键词[WOS]RISK
收录类别SCI
语种英语
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]
项目资助者Ningxia Key RD Program ; Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis ; Beijing Municipal Science and Technology Project
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000851954200001
出版者MDPI
引用统计
被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50111
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.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, 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.
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