Mineral prospectivity prediction using interval neutrosophic sets
Kraipeerapun, P., Brown, W., Fung, C.C. and Wong, K.W. (2006) Mineral prospectivity prediction using interval neutrosophic sets. In: IASTED International Conference on Artificial Intelligence and Applications, AIA 2006, 13-16 Feb. 2006, Innsbruck, Austria.
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.
|Publication Type:||Conference Paper|
|Murdoch Affiliation:||School of Information Technology|
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