Sensors, Vol. 19, Pages 4411: Animal Movement Prediction Based on Predictive Recurrent Neural Network
Sensors, Vol. 19, Pages 4411: Animal Movement Prediction Based on Predictive Recurrent Neural Network
Sensors doi: 10.3390/s19204411
Authors:
Jehyeok Rew
Sungwoo Park
Yongjang Cho
Seungwon Jung
Eenjun Hwang
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 ...
Source: Sensors - Category: Biotechnology Authors: Jehyeok Rew Sungwoo Park Yongjang Cho Seungwon Jung Eenjun Hwang Tags: Article Source Type: research
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