Data Mining Improves Pipeline Risk Assessment

DOI

10.2991/iccsee.2013.668How to use a DOI?

Keywords

risk assessment, data mining, pipeline risk

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baoqin Wang
AU  - Xuyang Zhou
AU  - Wenjing Zhang
PY  - 2013/03
DA  - 2013/03
TI  - Data Mining Improves Pipeline Risk Assessment
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2683
EP  - 2686
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.668
DO  - 10.2991/iccsee.2013.668
ID  - Wang2013/03
ER  -