Machine learning for modeling animal movement.

ABSTRACT: Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal's velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare p

SUBMITTER: Wijeyakulasuriya DA 

PROVIDER: S-EPMC7384613 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature