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A prediction and imputation method for marine animal movement data

Citation

Li, XQ and Sindihebura, TT and Zhou, L and Duarte, CM and Costa, DP and Hindell, MA and McMahon, C and Muelbert, MMC and Zhang, X and Peng, C, A prediction and imputation method for marine animal movement data, PeerJ Computer Science, 7 Article e656. ISSN 2376-5992 (2021) [Refereed Article]


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Copyright Statement

2021. Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

DOI: doi:10.7717/PEERJ-CS.656

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:Marine animal movement, trajectory analysis, prediction, imputation
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Data mining and knowledge discovery
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Hindell, MA (Professor Mark Hindell)
ID Code:152063
Year Published:2021
Deposited By:Information and Communication Technology
Deposited On:2022-08-10
Last Modified:2022-09-01
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