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Comparing the performance of different neural networks architectures for the prediction of mineral prospectivity

Fung, C.C.ORCID: 0000-0001-5182-3558, Iyer, V., Brown, W. and Wong, K.W. (2005) Comparing the performance of different neural networks architectures for the prediction of mineral prospectivity. In: 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, 18-21 Aug. 2005, Guangzhou, China pp. 394-398.

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In the mining industry, effective use of geographic information systems (CIS) to identify new geographic locations that are favorable for mineral exploration is very important. However, definitive prediction of such location is not an easy task. In this paper, four different neural networks, namely, the Polynomial Neural Network (PNN), General Regression Neural Network (GRNN), Probabilistic Neural Network (PrNN) and Back Propagation Neural Network (BPNN) have been used to classify data corresponding to cells in a map grid into deposit cells and barren cells. These approaches were tested on the GIS mineral exploration data from the Kalgoorlie region of Western Australia. The performance of individual neural networks is compared based on simulation results. The results demonstrate various degrees of success for the networks and suggestions on how to integrate the results are discussed.

Item Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: IEEE
Copyright: © 2005 IEEE
Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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