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dc.contributor.authorShanmuganathan, S
dc.contributor.authorSallis, P
dc.contributor.authorBuckeridge, J
dc.date.accessioned2011-08-14T22:47:42Z
dc.date.available2011-08-14T22:47:42Z
dc.date.copyright2003
dc.date.issued2011-08-15
dc.identifier.citationMODSIM 2003, International Congress on Modelling and Simulation (Integrative Modelling of Biophysical, Social and Economic Systems for Resource Management Solutions), in Townsville, Australia. 14-17 July 2003 Volume 2 pp. 759-764
dc.identifier.urihttp://hdl.handle.net/10292/1727
dc.description.abstractOld and new ecological models can be classified into two basic categories: Those aimed at (i) gaining more insight into ecological systems and (ii) producing predictive models of ecosystem behaviour. Many of the models successfully applied to ecological modelling are borrowed from other disciplines such as engineering, mathematics and in recent times from intelligent information processing systems motivated by neuro-physiological understandings i.e. 1 artificial neural networks (ANNs). The use of ANNs in ecological modelling is becoming a popular method with considerable success in elucidating the complexity in ecosystem processes. We critically analyse some ecological modelling applications with self-organising maps (SOMs), within the connectionist neural computing paradigms. These are used to unravel the non-linear relationships in highly complex and often cryptic ecosystems from northern New Zealand. A need to accurately predict an ecosystems response to daily increasing human influences on the environment and its biodiversity is considered to be absolutely vital to preserve natural systems. The example illustrated shows SOM abilities to extract more knowledge from the ecological monitoring data of complex matrices with numeric values of environmental and biological indicators, compared to the conventional data analysis methods. Conventional methods are seen as of little use in exploring the non-linear relationships within the data.
dc.publisherModelling & Simulation Society of Australia & New Zealand Inc.
dc.relation.isreplacedby10292/5277
dc.relation.isreplacedbyhttp://hdl.handle.net/10292/5277
dc.relation.urihttp://www.mssanz.org.au/MODSIM03/Volume_02/A14/08_Shanmuganathan.pdf
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version)
dc.subjectEcological modelling
dc.subjectSelf-organising maps
dc.subjectEcological data
dc.titleEcological modelling with self-organising maps
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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