Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models

Accurate burst pressure prediction is critical for ensuring oil and gas pipeline safety, guiding maintenance decisions, and lowering costs and risks. Traditional methods have limitations, including high experimental costs, conservative empirical models, and computationally expensive numerical algori...

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Bibliografische Detailangaben

Hauptverfasser:	Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Masdi B. Muhammad, Mohamad Hanif Md Saad, Najeebullah Lashari, Muhammad Hussain, Abdul Sattar Palli
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Elsevier 2024-12-01
Schriftenreihe:	Results in Engineering
Schlagworte:	Burst pressure prediction Machine learning SMOTE Data augmentation Oil and gas pipelines Safety
Online-Zugang:	http://www.sciencedirect.com/science/article/pii/S2590123024014877
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