A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization
Joint Authors
Olatunji, Sunday Olusanya
Owolabi, Taoreed O.
Akande, Kabiru O.
Abdulraheem, AbdulAzeez
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms.
Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized.
In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR).
The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor.
The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry.
The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability.
The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir.
Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system.
American Psychological Association (APA)
Akande, Kabiru O.& Owolabi, Taoreed O.& Olatunji, Sunday Olusanya& Abdulraheem, AbdulAzeez. 2016. A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1094894
Modern Language Association (MLA)
Akande, Kabiru O.…[et al.]. A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1094894
American Medical Association (AMA)
Akande, Kabiru O.& Owolabi, Taoreed O.& Olatunji, Sunday Olusanya& Abdulraheem, AbdulAzeez. A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1094894
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094894