@inproceedings{discovery10120848,
           pages = {4382--4388},
       booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special track on AI for CompSust and Human well-being},
         address = {Yokohama, Japan},
            note = {{\copyright} 2020, IJCAI This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.},
       publisher = {IJCAI International Joint Conferences on Artificial Intelligence Organization},
           title = {Forecasting Avian Migration Patterns using a Deep Bidirectional RNN Augmented with an Auxiliary Task},
            year = {2021},
         journal = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
           month = {January},
             url = {http://dx.doi.org/10.24963/ijcai.2020/604},
        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.},
          author = {Owoeye, K},
        keywords = {Data Mining: Mining Spatial, Temporal Data,
Multidisciplinary Topics and Applications: AI for Life Science,
Machine Learning: Classification, 
Machine Learning: Deep Learning: Sequence Modeling}
}