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Testing the efficacy of unsupervised machine learning techniques to infer shark behaviour from accelerometry data

Norris, Courtney E. (2019) Testing the efficacy of unsupervised machine learning techniques to infer shark behaviour from accelerometry data. Honours thesis, Murdoch University.

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Biologging is becoming a powerful tool in the study of free-ranging animal behaviour. Accelerometers play an important role particularly for cryptic aquatic species by facilitating the measurement of animal body movement and thus, behaviour. However, our ability to collect large and complex data sets is surpassing our ability to analyse them, prompting a need to develop methodologies for automated behavioural classification. Unsupervised machine learning is particularly useful for behavioural classification where direct observations to link patterns of acceleration to animal behaviour are not always attainable. We tested the ability of unsupervised machine learning to classify shark behaviour by applying two common unsupervised approaches, K-means clustering and Hidden Markov models (HMM), to ground-truthed accelerometry data collected from captive juvenile lemon sharks (Negaprion brevirostris). Although K-means clustering demonstrated low classification performance, the HMM performed well in distinguishing broad categories in behaviour (resting vs swimming), but generally had poor performance in rare and more complex behaviours (e.g. prey handling or burst swimming). This study is one of the first to validate the use of common unsupervised machine learning algorithms and lends further support to their use in the study of behaviour in free-ranging animals, while also showing limitations in their ability to discern complex behaviours.

Item Type: Thesis (Honours)
Murdoch Affiliation(s): School of Veterinary and Life Sciences
Supervisor(s): Gleiss, Adrian and Armstrong, Nicola
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