Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

Data Mining Improves Pipeline Risk Assessment

Authors
Baoqin Wang, Xuyang Zhou, Wenjing Zhang
Corresponding Author
Baoqin Wang
Available Online March 2013.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - https://doi.org/10.2991/iccsee.2013.668
ID  - Wang2013/03
ER  -