Mineral prospectivity prediction using interval neutrosophic sets

Authors:
Pawalai Kraipeerapun;Chun Che Fung;Warick Brown;Kok-Wai Wong
Affiliations:
School of Information Technology, Murdoch University, Australia;Centre for Enterprise Collaboration in Innovative Systems, Murdoch University, Australia;Centre for Exploration Targeting, The University of Western Australia, Australia;School of Information Technology, Murdoch University, Australia
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 0
Cited 3

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
Granular computing neural-fuzzy modelling: A neutrosophic approach

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The integration of Geographic Information Systems (GIS) data with neural networks can improve the accuracy of predictive maps showing the favourability for mineral deposits at regional-scale. However, uncertainty is rarely estimated for mineral prospectivity maps. Quantification of uncertainty is very important as it can enhance prediction and supports decision making. This paper proposes an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Interval neutrosophic sets are combined with neural networks to classify map cells as either deposits or barren based on input feature vectors representing exploration data. In this paper, two feed-forward backpropagation neural networks are used to determine truth-membership, indeterminacy-membership, and false-membership values, which together form an interval neutrosophic set. The INS model improves classification performance compared to a threshold method and has the advantage of providing a measure of the uncertainty of the classification.