Next Article in Journal
Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
Previous Article in Journal
Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning
Previous Article in Special Issue
The Potential of Utilizing Air Temperature Datasets from Non-Professional Meteorological Stations in Brno and Surrounding Area
Open AccessArticle

Animal Movement Prediction Based on Predictive Recurrent Neural Network

School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4411; https://doi.org/10.3390/s19204411
Received: 29 July 2019 / Revised: 30 September 2019 / Accepted: 9 October 2019 / Published: 11 October 2019
Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement. View Full-Text
Keywords: animal movement; movement prediction; pattern prediction; predictive recurrent neural networks; kernel density image animal movement; movement prediction; pattern prediction; predictive recurrent neural networks; kernel density image
Show Figures

Figure 1

MDPI and ACS Style

Rew, J.; Park, S.; Cho, Y.; Jung, S.; Hwang, E. Animal Movement Prediction Based on Predictive Recurrent Neural Network. Sensors 2019, 19, 4411.

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
Back to TopTop