Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220
- 24 August 2022
Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent
pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal
corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of
pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm
to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into
training and test set More >
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