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Identifying Hotspots in Natural Gas and Oil Pipeline Networks and Understanding Incident Risk Factors: An Integrated Spatial Analysis and Machine Learning Approach

dc.contributor.authorAsaye, Lemlem
dc.contributor.authorLe, Chau
dc.contributor.authorLe, Trung
dc.contributor.authorHuang, Ying
dc.contributor.authorYadav, Om Prakash
dc.contributor.authorLe, Tuyen
dc.date.accessioned2026-08-20T12:05:26Z
dc.date.available2026-08-20T12:05:26Z
dc.date.copyright2025/07/22
dc.date.issued2025
dc.identifier.otherJPSEA2.PSENG-1894.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313084
dc.description.abstractAbstractPipeline networks are crucial for transporting natural gas and oil but are vulnerable to significant safety and environmental risks. Previous studies have primarily focused on identifying overall incident causes without examining how spatial ...
dc.publisherAmerican Society of Civil Engineers
dc.titleIdentifying Hotspots in Natural Gas and Oil Pipeline Networks and Understanding Incident Risk Factors: An Integrated Spatial Analysis and Machine Learning Approach
dc.typeJournal Article
dc.journal.volume16
dc.journal.issue4
dc.journal.titleJournal of Pipeline Systems Engineering and Practice
dc.identifier.doi10.1061/JPSEA2.PSENG-1894
dc.journal.fristpage04025070-1
dc.journal.lastpage04025070-20
dc.page20
dc.treeJournal of Pipeline Systems Engineering and Practice:;2025:;Volume ( 016 ):;issue: 004
dc.contenttypeFulltext


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