University Of Tasmania
152063-A prediction and imputation method for marine animal movement data.pdf (3.07 MB)
Download file

A prediction and imputation method for marine animal movement data

Download (3.07 MB)
journal contribution
posted on 2023-05-21, 11:24 authored by Li, XQ, Sindihebura, TT, Zhou, L, Duarte, CM, Costa, DP, Mark HindellMark Hindell, McMahon, C, Muelbert, MMC, Zhang, X, Peng, C
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.


Publication title

PeerJ Computer Science



Article number









Institute for Marine and Antarctic Studies


PeerJ, Ltd.

Place of publication

United Kingdom

Rights 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 ( 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.

Repository Status

  • Open

Socio-economic Objectives

Artificial intelligence