By Topic

Quantification of Uncertainty in Mineral Prospectivity Prediction Using Neural Network Ensembles and Interval Neutrosophic Sets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
P. Kraipeerapun ; School of Information Technology, Murdoch University, Australia. email: ; Kok Wai Wong ; Chun Che Fung ; W. Brown

Quantification of uncertainty in mineral prospectivity prediction is an important process to support decision making in mineral exploration. Degree of uncertainly 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 arc created for each network architecture to predict degrees of favourability for deposit and non deposit, which are represented by truth and false membership 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.

Published in:

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Date of Conference:

0-0 0