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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2012

01-09-2012 | Original Article

Application of an expert system to predict thermal conductivity of rocks

Author: Manoj Khandelwal

Published in: Neural Computing and Applications | Issue 6/2012

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Abstract

In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by SVM and MVRA was 0.994 and 0.918, respectively, whereas MAE was 0.0453 and 0.2085 for SVM and MVRA, respectively.
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Metadata
Title
Application of an expert system to predict thermal conductivity of rocks
Author
Manoj Khandelwal
Publication date
01-09-2012
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 6/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0573-y

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