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Quantification of uncertainty in mineral prospectivity prediction using neural network ensembles and interval neutrosophic sets

Kraipeerapun, P., Wong, K.W., Fung, C.C. and Brown, W. (2006) Quantification of uncertainty in mineral prospectivity prediction using neural network ensembles and interval neutrosophic sets. In: 2006 International Joint Conference on Neural Networks, 16-21 July 2006, Vancover, B.C, Canada pp. 3034-3039.

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    Link to Published Version: http://dx.doi.org/10.1109/IJCNN.2006.247262
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    Abstract

    Quantification of uncertainty in mineral prospectivity prediction is an important process to support decision making in mineral exploration. Degree of uncertainty can identify level of quality in the prediction. This paper proposes an approach to predict degrees of favourability for gold deposits together with quantification of uncertainty in the prediction. Geographic Information Systems (GIS) data is applied to the integration of ensemble neural networks and interval neutrosophic sets. Three different neural network architectures are used in this paper. The prediction and its uncertainty are represented in the form of truth-membership, indeterminacy-membership, and false-membership values. Two networks are created for each network architecture to predict degrees of favourability for deposit and non deposit, which are represented by truth and false memhership values respectively. Uncertainty or indeterminacy-membership values are estimated from both truth and false membership values. The results obtained using different neural network ensemble techniques are discussed in this paper.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: (c) 2006 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.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/602
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