Data augmentation using conditional generative adversarial network (cGAN): Application for prediction of corrosion pit depth and testing using neural network
Machine learning (ML) based algorithms, due to their ability to model nonlinear and complex relationship, have been used in predicting corrosion pit depth in oil and gas pipelines. Class imbalance and data scarcity are the challenging problems while training ML models. This paper utilized a conditio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2023-03-01
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| Series: | Journal of Pipeline Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667143322000634 |