Next Article in Journal
Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model
Previous Article in Journal
Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives
Open AccessArticle

Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

1
College of Information Science and Engineering, Ningbo University, Ningbo 315211, China
2
Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, China
3
Red Sea Research Center, King Abdullah University of Science & Technology, Thuwal 23955-6900, Saudi Arabia
4
Department of Ecology & Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA
5
Centre d’Études Biologiques de Chizé, UMR 7372 CNRS-Université de La Rochelle, 79360 Villiers-en-Bois, France
6
Department of Biological Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
7
Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 05, Tasmania 7001, Australia
8
Sydney Institute of Marine Science, 19 Chowder Bay Road, Mosman, New South Wales 2088, Australia
9
Instituto de Oceanografia, Caixa Postal 474, Rio Grande 96201-900, Brazil
10
Australian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia (M096), 35 Stirling Highway, Crawley, Western Australia 6009, Australia
11
Department of Computer Science, City University of Hong Kong, Hong Kong, China
12
Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science & Technology, Thuwal 23955-6900, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2935; https://doi.org/10.3390/app9142935
Received: 12 June 2019 / Revised: 14 July 2019 / Accepted: 16 July 2019 / Published: 23 July 2019
The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research. View Full-Text
Keywords: marine animal movement analysis; recurrent neural networks; representative patterns marine animal movement analysis; recurrent neural networks; representative patterns
Show Figures

Figure 1

MDPI and ACS Style

Peng, C.; Duarte, C.M.; Costa, D.P.; Guinet, C.; Harcourt, R.G.; Hindell, M.A.; McMahon, C.R.; Muelbert, M.; Thums, M.; Wong, K.-C.; Zhang, X. Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species. Appl. Sci. 2019, 9, 2935.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop