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Development of Machine Learning Models for Multiple Leak Detection in Offshore Gas Pipelines under Single- and Multiphase Flow Conditions

dc.contributor.authorAl-Ammari, Wahib A.
dc.contributor.authorSleiti, Ahmad K.
dc.contributor.authorAbuShanab, Yazeed
dc.contributor.authorHamilton, M.
dc.contributor.authorRashid Hasan, Abu
dc.contributor.authorHassan, Ibrahim
dc.contributor.authorKaan, M. S.
dc.contributor.authorRezaei-Gomari, S.
dc.contributor.authorRahman, Mohammad Azizur
dc.date.accessioned2026-08-20T12:04:28Z
dc.date.available2026-08-20T12:04:28Z
dc.date.copyright2025/09/27
dc.date.issued2026
dc.identifier.otherJPSEA2.PSENG-1813.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313061
dc.description.abstractAbstractLeak detection and localization in offshore gas pipelines is critical in preventing hazardous events and minimizing operational and economic losses. This study presents a structured machine learning (ML) framework capable of detecting and ...
dc.publisherAmerican Society of Civil Engineers
dc.titleDevelopment of Machine Learning Models for Multiple Leak Detection in Offshore Gas Pipelines under Single- and Multiphase Flow Conditions
dc.typeJournal Article
dc.journal.volume17
dc.journal.issue1
dc.journal.titleJournal of Pipeline Systems Engineering and Practice
dc.identifier.doi10.1061/JPSEA2.PSENG-1813
dc.journal.fristpage04025092-1
dc.journal.lastpage04025092-20
dc.page20
dc.treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 001
dc.contenttypeFulltext


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