Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

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 with a 9:1 ratio. The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search, and the test set was used to determine the performance of the model. The results showed that the Mean Absolute Error (MAE) of this framework is 28.06% of the traditional model and outperforms other ensemble methods. Therefore, the proposed framework is suitable for metal corrosion prediction under small sample conditions.