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Activity Number: 311 - Statistical Models in Ecology
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #329802 Presentation
Title: Machine Learning Methods for Animal Movement
Author(s): Dhanushi A Wijeyakulasuriya* and Ephraim Hanks and Benjamin Shaby
Companies: Pennsylvania State University and The Pennsylvania State University and Penn State University
Keywords: deep learning; animal movement; time series

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. Deep learning algorithms are powerful and flexible predictive modeling tools, but have never been applied to animal movement data. In this study we present an ensemble neural networks approach to modeling and predicting animal movement. Our approach considers predicting an animal's displacement at each time step using the position and velocity of the animal at several previous time lags. We apply this approach to ant movement data.

Authors who are presenting talks have a * after their name.

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