Data Mining Improves Pipeline Risk Assessment

Abstract

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.

Original document

The different versions of the original document can be found in:

• http://download.atlantis-press.com/php/download_paper.php?id=5099 under the license cc-by

• http://dx.doi.org/10.2991/iccsee.2013.668

• http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2168

• https://www.scientific.net/AMM.347-350.2168,

https://www.atlantis-press.com/proceedings/iccsee-13/5099,

https://academic.microsoft.com/#/detail/2001485629

• https://www.scientific.net/AMM.347-350.2168.pdf,

http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2168

DOIS: 10.4028/www.scientific.net/amm.347-350.2168 10.2991/iccsee.2013.668