Ensemble learning: A new method for predicting corrosion rates under small sample of metal corrosion data in the laboratory

Abstract

Accurate prediction of the internal corrosion rates of oil and gas pipelines is an urgent need in the oil industry at present, and laboratory-scale experiments are of great significance in achieving this goal. However, obtaining large amounts of experimental data is both costly and time-consuming. Here, the possibility of using ensemble learning methods to reduce the corrosion rate prediction errors based on small datasets is investigated. The work presented in this paper focuses on the prediction accuracy and the influencing factors of bagging and boosting methods under 99 sets of laboratory data. The research we have done suggests that the bagging algorithm outperforms the boosting algorithm in scenarios where small samples of discrete data are used, and that the number, dimensionality, and dispersion of the training samples all have an impact on the prediction results. Further, the prediction error values obtained via ensemble learning methods are smaller compared to the results obtained using traditional empirical models such as Norsok M506. It is reasonable to conclude that the use of bagging algorithms in ensemble learning is suitable for obtaining accurate corrosion rate predictions for small laboratory datasets.