Interpretable machine learning for maximum corrosion depth and influence factor analysis
Interpretable machine learning for maximum corrosion depth and influence factor analysis
About this item
Full title
Author / Creator
Song, Yuhui , Wang, Qinying , Zhang, Xingshou , Dong, Lijin , Bai, Shulin , Zeng, Dezhi , Zhang, Zhi , Zhang, Huali and Xi, Yuchen
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
We have employed interpretable methods to uncover the black-box model of the machine learning (ML) for predicting the maximum pitting depth (
dmax
) of oil and gas pipelines. Ensemble learning (EL) is found to have higher accuracy compared with several classical ML models, and the determination coefficient of the adaptive boosting (AdaBoost)...
Alternative Titles
Full title
Interpretable machine learning for maximum corrosion depth and influence factor analysis
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_64e9e2462b034e51a8d69b901475c72a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_64e9e2462b034e51a8d69b901475c72a
Other Identifiers
ISSN
2397-2106
E-ISSN
2397-2106
DOI
10.1038/s41529-023-00324-x
