Data Mining Improves Pipeline Risk Assessment
Baoqin Wang, Xuyang Zhou, Wenjing Zhang
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.668How to use a DOI?
- risk assessment, data mining, pipeline risk
- 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.
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 - Conference 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 -