Open Access

ARTICLE

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

Yang Yang^1,2,*, Pengfei Zheng^3,4, Fanru Zeng⁵, Peng Xin⁶, Guoxi He¹, Kexi Liao¹
1 State Key Laboratory of Oil Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, China
2 School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu, 610500, China
3 Spatial Information Technology and Big Data Mining Research Center, School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu, 610500, China
4 Sichuan Xinyang Anchuang Technology Co., Ltd., Chengdu, 610500, China
5 Sichuan Water Conservancy College, Chengdu, 610500, China
6 CCDC Safety, Environment, Quality Supervision & Testing Research Institute, Guanghan, 618300, China
* Corresponding Author: Yang Yang. Email:
(This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)

Computer Modeling in Engineering & Sciences 2023, 134(1), 267-291. https://doi.org/10.32604/cmes.2022.020220

Received 11 November 2021; Accepted 24 February 2022; Issue published 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 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.

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Keywords

Oil pipeline; bagging; KNN; ensemble learning; small sample size

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