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Analysing coastal ocean model outputs using competitive-learning pattern recognition techniques
journal contribution
posted on 2023-05-18, 03:37 authored by Williams, RN, de Souza Jr, PA, Jones, EMTo assist in interpreting the hydrodynamics of a complex coastal environment, a Self Organizing Map (SOM) has been constructed using output from a three-dimensional hydrodynamic model of the Huon-D'Entrecasteaux region in South-East Tasmania, over a one-year period. Interpretation of the SOM enabled nine characteristic or prototype states to be identified. As expected, the dominant forcing mechanisms were freshwater input via riverine discharge and input from oceanic waters. While these mechanisms are well understood, subtle features associated with the interaction of the two forcing mechanisms and the transitions between meta-stable states, were revealed by visualizing the SOM output. Further investigation was undertaken to determine how effective the SOM would be in identifying these prototype states given sensor data from a sensor network being designed for future deployment within the region. This research has demonstrated that SOM analysis can be a useful tool for identifying and interpreting patterns in large oceanographic datasets.
History
Publication title
Environmental Modelling and SoftwareVolume
57Pagination
165-176ISSN
1364-8152Department/School
School of Information and Communication TechnologyPublisher
Elsevier Sci LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1GbRights statement
Copyright 2014 ElsevierRepository Status
- Restricted