Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique  

作　　者：Yang Yang Pengfei Zheng Fanru Zeng Peng Xin Guoxi He Kexi Liao 

机构地区：[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]SichuanWater Conservancy College,Chengdu,610500,China [6]CCDC Safety,Environment,Quality Supervision&Testing Research Institute,Guanghan,618300,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第1期267-291,共25页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52174062).

摘　　要：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 theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ensemblemethods.Therefore,the proposed framework is suitable formetal corrosion prediction under small sample conditions.

关 键 词：Oil pipeline BAGGING KNN ensemble learning small sample size 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]