Analysing coastal ocean model outputs using competitive-learning pattern recognition techniques

To 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.