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dc.contributor.authorLi, Xinqing
dc.contributor.authorSindihebura, Tanguy Tresor
dc.contributor.authorZhou, Lei
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorCosta, Daniel P.
dc.contributor.authorHindell, Mark A.
dc.contributor.authorMcMahon, Clive
dc.contributor.authorMuelbert, Mônica M.C.
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorPeng, Chengbin
dc.date.accessioned2021-08-09T08:35:22Z
dc.date.available2021-08-09T08:35:22Z
dc.date.issued2021-08-03
dc.date.submitted2021-04-01
dc.identifier.citationLi, X., Sindihebura, T. T., Zhou, L., Duarte, C. M., Costa, D. P., Hindell, M. A., … Peng, C. (2021). A prediction and imputation method for marine animal movement data. PeerJ Computer Science, 7, e656. doi:10.7717/peerj-cs.656
dc.identifier.issn2376-5992
dc.identifier.doi10.7717/peerj-cs.656
dc.identifier.urihttp://hdl.handle.net/10754/670496
dc.description.abstractData prediction and imputation are important parts of marine animal movement trajectory analysis as they can help researchers understand animal movement patterns and address missing data issues. Compared with traditional methods, deep learning methods can usually provide enhanced pattern extraction capabilities, but their applications in marine data analysis are still limited. In this research, we propose a composite deep learning model to improve the accuracy of marine animal trajectory prediction and imputation. The model extracts patterns from the trajectories with an encoder network and reconstructs the trajectories using these patterns with a decoder network. We use attention mechanisms to highlight certain extracted patterns as well for the decoder. We also feed these patterns into a second decoder for prediction and imputation. Therefore, our approach is a coupling of unsupervised learning with the encoder and the first decoder and supervised learning with the encoder and the second decoder. Experimental results demonstrate that our approach can reduce errors by at least 10% on average comparing with other methods.
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (NO. 61802372), the Natural Science Foundation of Zhejiang Province (NO. LGG20F020011), the Ningbo Science and Technology Innovation Project (NO. 2018B10080), and the Qianjiang Talent Plan (NO. QJD1702031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.publisherPeerJ
dc.relation.urlhttps://peerj.com/articles/cs-656
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA prediction and imputation method for marine animal movement data
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalPeerJ Computer Science
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollege of Information Science and Engineering, Ningbo University, Ningbo, China
dc.contributor.institutionDepartment of Ecology & Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, United States of America
dc.contributor.institutionInstitute for Marine and Antarctic Studies, University of Tasmania, Tasmania, Australia
dc.contributor.institutionSydney Institute of Marine Science, Mosman, Australia
dc.contributor.institutionInstituto de Oceanografia, Rio Grande, Brazil
dc.contributor.institutionNingbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, Zhejiang, China
dc.identifier.volume7
dc.identifier.pagese656
kaust.personDuarte, Carlos M.
kaust.personZhang, Xiangliang
dc.date.accepted2021-07-10
refterms.dateFOA2021-08-09T08:35:57Z


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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.