Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment

V. Iyer; C. C. Fung; W. Brown; T. Gedeon

Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment

V. Iyer^*, C. C. Fung, W. Brown, T. Gedeon

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

In 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.

Original language	English
Pages	B411-B414
Publication status	Published - 2004
Event	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand Duration: 21 Nov 2004 → 24 Nov 2004

Conference

Conference	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering
Country/Territory	Thailand
City	Chiang Mai
Period	21/11/04 → 24/11/04

Cite this

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title = "Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment",

abstract = "In 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.",

keywords = "Geographical Information System, Mineral prospectivity, Polynomial neural network",

author = "V. Iyer and Fung, {C. C.} and W. Brown and T. Gedeon",

year = "2004",

language = "English",

pages = "B411--B414",

note = "IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering ; Conference date: 21-11-2004 Through 24-11-2004",

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T1 - Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment

AU - Iyer, V.

AU - Fung, C. C.

AU - Brown, W.

AU - Gedeon, T.

PY - 2004

Y1 - 2004

N2 - In 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.

AB - In 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.

KW - Geographical Information System

KW - Mineral prospectivity

KW - Polynomial neural network

UR - http://www.scopus.com/inward/record.url?scp=27944449723&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:27944449723

SP - B411-B414

T2 - IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering

Y2 - 21 November 2004 through 24 November 2004

ER -

Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment

Abstract

Conference

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