RETRACTED: Data Mining Improves Pipeline Risk Assessment

Bao Qin Wang; Xu Yang Zhou; Wen Jing Zhang

doi:10.4028/www.scientific.net/AMM.347-350.2168

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RETRACTED: Data Mining Improves Pipeline Risk Assessment
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350RETRACTED: Data Mining Improves Pipeline Risk...

RETRACTED: Data Mining Improves Pipeline Risk Assessment

Retracted:

This paper has been retracted by publisher.
This paper was found to be in violation of the scope and quality criteria. The document is now considered retracted. Due to strong violation, necessary effort should be made to remove all further references to this paper.
We regret any inconvenience this publication might cause you.

Abstract:

Retracted paper: Accidents to pipelines have been recorded and they often result in catastrophic consequences for environment and society with a great deal of economic loss. Standard methods of evaluating pipeline risk have stressed index-based and conditional based data assessment processes. Data mining represents a shift from verification-driven data analysis approaches to discovery-driven methods in risk assessment.

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Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

2168-2172

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.2168

Online since:

August 2013

Authors:

Bao Qin Wang, Xu Yang Zhou, Wen Jing Zhang

Keywords:

Data Mining (DM), Pipeline Risk, Risk Assessment

Export:

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Permissions:

Creative Commons CC BY 4.0

Citation:

References

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