A prediction and imputation method for marine animal movement data
Type
ArticleAuthors
Li, XinqingSindihebura, Tanguy Tresor
Zhou, Lei
Duarte, Carlos M.

Costa, Daniel P.
Hindell, Mark A.
McMahon, Clive
Muelbert, Mônica M.C.
Zhang, Xiangliang

Peng, Chengbin
KAUST Department
Biological and Environmental Science and Engineering (BESE) DivisionComputer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Marine Science Program
Red Sea Research Center (RSRC)
Date
2021-08-03Submitted Date
2021-04-01Permanent link to this record
http://hdl.handle.net/10754/670496
Metadata
Show full item recordAbstract
Data 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.Citation
Li, 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.656Sponsors
This 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.Publisher
PeerJJournal
PeerJ Computer ScienceAdditional Links
https://peerj.com/articles/cs-656ae974a485f413a2113503eed53cd6c53
10.7717/peerj-cs.656
Scopus Count
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