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Identifying Prototype States within Hydrodynamic Model Outputs using a Self-Organising Feature Map
conference contribution
posted on 2023-05-23, 17:26 authored by Williams, RN, de Souza, P, Jones, E, D'Este, CThe Coastal Environmental Modelling Team at CSIRO Marine and Atmospheric Research, in Hobart, Tasmania, Australia, has been modelling hydrodynamic conditions within the estuarine environment of south-eastern Tasmania for sev- eral years. Historical model output has been analysed in an effort to identify prototype hydrodynamic states (i.e., frequently encountered typical hydrodynamic situations) exhibited by the estuarine environment over that period. A competitive-learning neural network, the Self-Organizing Feature Map (SOM), was used to identify these prototype states. Once such a network has been trained, each node in its output layer represents a particular pattern in the input data and nodes representing similar patterns are located near to each other on the two-dimensional output grid, while those representing dissimilar patterns are further apart. Estimated daily average surface hydrodynamic conditions (salinity, temperature and ocean current components) within the south-east Tasmanian estuarine environment, from August 2009 to August 2010, were derived from output provided by the hydrodynamic model. The current components were then analysed using a SOM and subsequent inspection of the SOM output grid enabled a number of prototypical hydrodynamic states to be identified within the model outputs.
History
Publication title
Proceedings - Oceans 2012Editors
Kang, CGPagination
EJISBN
978-1-4577-2090-1Department/School
School of Information and Communication TechnologyPublisher
IEEEPlace of publication
Piscataway, NJ, USAEvent title
Oceans 2012 MTS/IEEEEvent Venue
YEOSU, KOREADate of Event (Start Date)
2012-05-21Date of Event (End Date)
2012-05-24Repository Status
- Restricted