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Using Machine Learning Techniques to Analyze Acoustic Doppler Current Profiler Data
  • Kristen Falcinelli,
  • Osama Abuomar
Kristen Falcinelli

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Osama Abuomar
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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 were discovered. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index were used to determine the optimal number of clusters in the ADCP dataset. These techniques proved to be useful in analysis of ADCP data and may be of further use in the oceanographic field.