eprintid: 10120848
rev_number: 15
eprint_status: archive
userid: 608
dir: disk0/10/12/08/48
datestamp: 2021-02-08 10:58:14
lastmod: 2021-02-08 13:58:59
status_changed: 2021-02-08 10:58:14
type: proceedings_section
metadata_visibility: show
creators_name: Owoeye, K
title: Forecasting Avian Migration Patterns using a Deep Bidirectional RNN Augmented with an Auxiliary Task
ispublished: pub
divisions: UCL
divisions: A01
divisions: B04
divisions: C05
divisions: F48
keywords: Data Mining: Mining Spatial, Temporal Data,
Multidisciplinary Topics and Applications: AI for Life Science,
Machine Learning: Classification, 
Machine Learning: Deep Learning: Sequence Modeling
note: © 2020, IJCAI This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Early forecasting of bird migration patterns has important application for example in reducing avian biodiversity loss. An estimated 100 million to 1 billion birds are known to die yearly during migration due to fatal collisions with human made infrastructures such as buildings, high tension lines, wind turbines and aircrafts thus raising a huge concern for conservationists. Building models that can forecast accurate migration patterns is therefore important to enable the optimal management of these critical infrastructures with the sole aim of reducing biodiversity loss. While previous works have largely focused on the task of forecasting migration intensities and the onset of just one migration state, predicting several migration states at even finer granularity is more useful towards optimally managing the infrastructures that causes these deaths. In this work, we consider the task of forecasting migration patterns of the popular Turkey Vulture (Cathartes aura) collected with the aid of satellite telemetry for multiple years at a resolution of one hour. We use a deep Bidirectional-GRU recurrent neural network augmented with an auxiliary task where the state information of one layer is used to initialise the other. Empirical results on a variety of experiments with our approach show we can accurately forecast migration up to one week in advance performing better than a variety of baselines.
date: 2021-01-15
date_type: published
publisher: IJCAI International Joint Conferences on Artificial Intelligence Organization
official_url: http://dx.doi.org/10.24963/ijcai.2020/604
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1842608
doi: 10.24963/ijcai.2020/604
isbn_13: 9780999241165
lyricists_name: Owoeye, Kehinde
lyricists_id: OWOEY11
actors_name: Owoeye, Kehinde
actors_id: OWOEY11
actors_role: owner
full_text_status: public
publication: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
place_of_pub: Yokohama, Japan
pagerange: 4382-4388
event_title: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
institution: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
book_title: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special track on AI for CompSust and Human well-being
citation:        Owoeye, K;      (2021)    Forecasting Avian Migration Patterns using a Deep Bidirectional RNN Augmented with an Auxiliary Task.                     In:  Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special track on AI for CompSust and Human well-being.  (pp. pp. 4382-4388).  IJCAI International Joint Conferences on Artificial Intelligence Organization: Yokohama, Japan.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10120848/1/ijcai20-multiauthor.pdf