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...
| Published in: | Results in Engineering |
|---|---|
| Main Authors: | Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Masdi B. Muhammad, Mohamad Hanif Md Saad, Najeebullah Lashari, Muhammad Hussain, Abdul Sattar Palli |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024014877 |
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