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

Content Provider	IEEE Xplore Digital Library
Author	Kraipeerapun, P. Kok Wai Wong Chun Che Fung Brown, W.
Copyright Year	2006
Description	Author affiliation: Murdoch Univ., Murdoch (Kraipeerapun, P.; Kok Wai Wong)
Abstract	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.
Starting Page	3034
Ending Page	3039
File Size	2619589
Page Count	6
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247262
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Intelligent networks Minerals Neural networks Gold Australia Feedforward neural networks Geographic Information Systems Decision making Databases
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Neural Network Ensembles using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty

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

Neural Network Ensembles using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty

The use of a multilayer feedforward neural network for mineral prospectivity mapping

Neural network ensembles using interval neutrosophic sets and bagging for mineral prospectivity prediction and quantification of uncertainty (2006)

Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction

Mineral potential mapping using feed-forward neural networks

Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity

Lithofacies Classification from Well Log Data Using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty

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

Similar Documents

Neural Network Ensembles using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty

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

Neural Network Ensembles using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty

The use of a multilayer feedforward neural network for mineral prospectivity mapping

Neural network ensembles using interval neutrosophic sets and bagging for mineral prospectivity prediction and quantification of uncertainty (2006)

Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction

Mineral potential mapping using feed-forward neural networks

Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity

Lithofacies Classification from Well Log Data Using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty

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