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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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 |
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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 |
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