{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:09:30Z","timestamp":1774613370355,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T00:00:00Z","timestamp":1662163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningxia Key R&amp;D Program","award":["2020BFG02013"],"award-info":[{"award-number":["2020BFG02013"]}]},{"name":"Ningxia Key R&amp;D Program","award":["2021-04"],"award-info":[{"award-number":["2021-04"]}]},{"name":"Ningxia Key R&amp;D Program","award":["Z191100001419002"],"award-info":[{"award-number":["Z191100001419002"]}]},{"name":"Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis","award":["2020BFG02013"],"award-info":[{"award-number":["2020BFG02013"]}]},{"name":"Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis","award":["2021-04"],"award-info":[{"award-number":["2021-04"]}]},{"name":"Hunan Construction of Natural Resources Knowledge Graph Based on Intelligence Analysis","award":["Z191100001419002"],"award-info":[{"award-number":["Z191100001419002"]}]},{"name":"Beijing Municipal Science and Technology","award":["2020BFG02013"],"award-info":[{"award-number":["2020BFG02013"]}]},{"name":"Beijing Municipal Science and Technology","award":["2021-04"],"award-info":[{"award-number":["2021-04"]}]},{"name":"Beijing Municipal Science and Technology","award":["Z191100001419002"],"award-info":[{"award-number":["Z191100001419002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14174391","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Knowledge Graph Representation Learning-Based Forest Fire Prediction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5170-576X","authenticated-orcid":false,"given":"Jiahui","family":"Chen","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1437-8288","authenticated-orcid":false,"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6535-477X","authenticated-orcid":false,"given":"Ling","family":"Peng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Luanjie","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-2832","authenticated-orcid":false,"given":"Xingtong","family":"Ge","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1071\/WF07014","article-title":"Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System","volume":"17","author":"Carvalho","year":"2008","journal-title":"Int. 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