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Using Machine Learning Techniques in the Analysis of Oceanographic Data
Abstract Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools that are capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and may demonstrate further use in the oceanographic field.
Faculty Advisor: Sam Abuomar, Computing Sciences
Graduate Student Mentor: Samantha Ladewig, Coastal and Marine Wetland Studies
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