Cargando…

Animal Movement Prediction Based on Predictive Recurrent Neural Network

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Rew, Jehyeok, Park, Sungwoo, Cho, Yongjang, Jung, Seungwon, Hwang, Eenjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832654/
https://www.ncbi.nlm.nih.gov/pubmed/31614699
http://dx.doi.org/10.3390/s19204411
_version_ 1783466224118661120
author Rew, Jehyeok
Park, Sungwoo
Cho, Yongjang
Jung, Seungwon
Hwang, Eenjun
author_facet Rew, Jehyeok
Park, Sungwoo
Cho, Yongjang
Jung, Seungwon
Hwang, Eenjun
author_sort Rew, Jehyeok
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6832654
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-68326542019-11-25 Animal Movement Prediction Based on Predictive Recurrent Neural Network Rew, Jehyeok Park, Sungwoo Cho, Yongjang Jung, Seungwon Hwang, Eenjun Sensors (Basel) Article 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. MDPI 2019-10-11 /pmc/articles/PMC6832654/ /pubmed/31614699 http://dx.doi.org/10.3390/s19204411 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rew, Jehyeok
Park, Sungwoo
Cho, Yongjang
Jung, Seungwon
Hwang, Eenjun
Animal Movement Prediction Based on Predictive Recurrent Neural Network
title Animal Movement Prediction Based on Predictive Recurrent Neural Network
title_full Animal Movement Prediction Based on Predictive Recurrent Neural Network
title_fullStr Animal Movement Prediction Based on Predictive Recurrent Neural Network
title_full_unstemmed Animal Movement Prediction Based on Predictive Recurrent Neural Network
title_short Animal Movement Prediction Based on Predictive Recurrent Neural Network
title_sort animal movement prediction based on predictive recurrent neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832654/
https://www.ncbi.nlm.nih.gov/pubmed/31614699
http://dx.doi.org/10.3390/s19204411
work_keys_str_mv AT rewjehyeok animalmovementpredictionbasedonpredictiverecurrentneuralnetwork
AT parksungwoo animalmovementpredictionbasedonpredictiverecurrentneuralnetwork
AT choyongjang animalmovementpredictionbasedonpredictiverecurrentneuralnetwork
AT jungseungwon animalmovementpredictionbasedonpredictiverecurrentneuralnetwork
AT hwangeenjun animalmovementpredictionbasedonpredictiverecurrentneuralnetwork