Assessment of uncertainty in mineral prospectivity prediction using interval neutrosophic set

Authors:
Pawalai Kraipeerapun;Chun Che Fung;Warick Brown
Affiliations:
School of Information Technology, Murdoch University, Australia;Centre for Enterprise Collaboration in Innovative Systems, Australia;Centre for Exploration Targeting, The University of Western Australia, Australia
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

Citing 0
Cited 2

Multiclass classification using neural networks and interval neutrosophic sets

CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Binary classification using ensemble neural networks and interval neutrosophic sets

Neurocomputing

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Abstract

Accurate spatial prediction of mineral deposit locations is essential for the mining industry. The integration of Geographic Information System (GIS) data with soft computing techniques can improve the accuracy of the prediction of mineral prospectivity. But uncertainty still exists. Uncertainties always exist in GIS data and in the processing required to make predictions. Quantification of uncertainty in mineral prospectivity prediction can support decision making in regional-scale mineral exploration. This research deals with these uncertainties. In this study, interval neutrosophic sets are combined with existing soft computing techniques to express uncertainty in the prediction of mineral deposit locations.