A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques

Maintaining the integrity of oil and gas pipelines is necessary for the efficient and safe transport of hydrocarbons. Corrosion defects can lead to decreased operational efficiency, leaks, a reduction in operational efficiency, and even catastrophic pipeline failures. Machine learning techniques are...

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Bibliographic Details

Published in:	Journal of Pipeline Science and Engineering
Main Authors:	Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab, Nasir Shafiq, Kamaludden Usman Danyaro, Nasir Khan, Adamu Tafida, Arsalaan Khan Yousafzai
Format:	Article
Language:	English
Published:	KeAi Communications Co. Ltd. 2025-06-01
Subjects:	Corrosion Defects Pipelines Meta-Learner Ensemble (MLE) Stacking Classifier Predictive Maintenance
Online Access:	http://www.sciencedirect.com/science/article/pii/S2667143324000714