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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brown, W.M., Gedeon, T.D., Groves, D.I., Barnes, R.G.: Artificial neural networks: a new method for mineral prospectivity mapping. Australian journal of earth sciences 47, 757–770 (2000)
Brown, W.: Artificial Neural Networks: a new method for mineral-prospectivity mapping. PhD thesis, University of Western Australia (2002)
Duckham, M.: Uncertainty and geographic information: computational and critical convergence. In: Representation in a digital geography, New York. John Wiley, Chichester (2002)
Fung, C.C., Iyer, V., Brown, W., Wong, K.W.: Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS (LNAI), vol. 3930. Springer, Heidelberg (2006)
Iyer, V., Fung, C.C., Brown, W., Gedeon, T.: A General Regression Neural Network Based Model for Mineral Favourability Prediction. In: Proceedings of the Fifth Postgraduate Electrical Engineering and Computing Symposium (PEECS 2004), Perth, Western Australia, September 2004, pp. 207–209 (2004)
Iyer, V., Fung, C.C., Brown, W., Gedeon, T.: Use of Polynomial Neural Network for a mineral prospectivity analysis in a GIS environment. In: Proceedings of the IEEE Region 10 conference (TenCon 2004), Chiang Mai, Thailand, November 2004, vol. B, pp. 411–414 (2004)
Skabar, A.: Mineral Potential Mapping using Feed-Forward Neural networks. In: Proceedings of the International Joint Conference on Neural Networks, July 2003, vol. 3, pp. 1814–1819 (2003)
Smarandache, F.: A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics. Xiquan, Phoenix, 3rd edn. (2003)
Wang, H., Smarandache, F., Zhang, Y.-Q., Sunderraman, R.: Interval Neutrosophic Sets and Logic: Theory and Applications in Computing. Neutrosophic Book Series, May 2005, vol. 5 (2005), http://arxiv.org/abs/cs/0505014
Wang, H.B., Madiraju, D., Zhang, Y.-Q., Sunderraman, R.: Interval Neutrosophic Sets. International Journal of Applied Mathematics and Statistics 3(M05), 1–18 (2005)
Wolfram, S.: Theory and Applications of Cellular Automata. World Scientific, Singapore (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kraipeerapun, P., Fung, C.C., Brown, W. (2005). Assessment of Uncertainty in Mineral Prospectivity Prediction Using Interval Neutrosophic Set. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_160
Download citation
DOI: https://doi.org/10.1007/11596981_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30819-5
Online ISBN: 978-3-540-31598-8
eBook Packages: Computer ScienceComputer Science (R0)