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<p>This research focuses on building classification models with multinomial responses based upon seven months of Automatic Identification System (AIS) data gathered from the South China Sea. The models, built using Gradient Boosted Machines (GBM), assess the validity of utilizing AIS to confirm an operating vessel’s origin, by country and geographical region. Two types of models are built. The first model captures the naturally dependent nature of AIS signals and serves as a proof of concept for how well a global model trained over many years could perform The second model attempts to reduce the dependency between AIS signals in order to characterize maritime patterns of behavior by country and region. With relative accuracy, both types of models are able to predict a vessel’s origin and provide insight into maritime patterns of behavior.
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<br>Subjects: data analysis; classification; Automatic Identification System; South China Sea; gradient boosted models</div>
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Cull, Kimberly M.</div>
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