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Use of Polynomial Neural Network for a mineral prospectivity analysis in a GIS environment

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dc.contributor.authorIyer, Vanaja
dc.contributor.authorFung, Chun Che
dc.contributor.authorBrown, Warwick
dc.contributor.authorGedeon, Tamas (Tom)
dc.coverage.spatialChiang Mai Thailand
dc.date.accessioned2015-12-08T22:29:37Z
dc.date.issued2004
dc.date.updated2015-12-08T09:23:03Z
dc.description.abstractIn the mining industry, identifying new geographic locations that are favourable for mineral exploration is very important. However definitive prediction of such locations is not an easy task. In the recent years artificial neural networks have received much attention in this area. This paper uses a class of neural networks known as the Polynomial Neural Network (PNN) to construct a model to correctly classify given location into deposit and barren areas. This model uses the Geographic Information Systems (GIS) data of the location. The method is tested on the GIS data for the Kalgoorlie region of Western Australia.
dc.identifier.isbn0780385608
dc.identifier.urihttp://hdl.handle.net/1885/34158
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE Region 10 Conference (TenCon 2004)
dc.sourceProceedings of the IEEE Region 10 Conference (TenCon 04)
dc.subjectKeywords: Geographical regions; Mineral exploration; Neural networks; Polynomials; Geographic locations; Polynomial neural networks; Geographic information systems Geographical Information System; Mineral prospectivity; Polynomial neural network
dc.titleUse of Polynomial Neural Network for a mineral prospectivity analysis in a GIS environment
dc.typeConference paper
local.bibliographicCitation.lastpage414
local.bibliographicCitation.startpage411
local.contributor.affiliationIyer, Vanaja, Murdoch University
local.contributor.affiliationFung, Chun Che, Murdoch University
local.contributor.affiliationBrown, Warwick, University of Western Australia
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080108 - Neural, Evolutionary and Fuzzy Computation
local.identifier.ariespublicationU3594520xPUB110
local.identifier.scopusID2-s2.0-27944449723
local.type.statusPublished Version

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