Feature Sensitivity and Robustness in Corrosion Rate Forecasting: A Comparative Study of Deterministic and Probabilistic Machine Learning Models

Feature Sensitivity and Robustness in Corrosion Rate Forecasting: A Comparative Study of Deterministic and Probabilistic Machine Learning Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Feature Sensitivity and Robustness in Corrosion Rate Forecasting: A Comparative Study of Deterministic and Probabilistic Machine Learning Models Jinlong Hu, Xiaoli Zhang, Shaofei Dong, Zhifu Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9145430/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate corrosion rate prediction is critical for ensuring industrial infrastructure safety and reducing economic losses, yet existing models often fail to balance precision, uncertainty quantification, and adaptability to dynamic scenarios. This study systematically compared the Extra Trees Regressor (ETR) and an Improved Bayesian Neural Network (ImprovedBNN) across static performance metrics (MSE, MAE, coverage), feature sensitivity to ±10% fluctuations, and statistical robustness via paired t-tests. The results show that ETR outperformed in point prediction (MSE=0.0051) while ImprovedBNN achieved perfect uncertainty coverage (1.000) with enhanced sensitivity to critical features, with both models demonstrating distinct engineering applicability. These findings establish a framework for model selection in corrosion management, reconciling deterministic precision and probabilistic rigor to inform practical decision-making. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Corrosion rate prediction Extra Trees Regressor (ETR) Improved Bayesian Neural Network (ImprovedBNN) Uncertainty quantification Feature sensitivity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9145430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":613576342,"identity":"69285e03-9daf-4939-99e6-109596c364b6","order_by":0,"name":"Jinlong Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACAwaGhAMMBSAmY+ODDxU2PPz8DcRoMQAxmZsNZ5xJk5GccYCgFhjJ3ibN23bYxqAhAb8Wc4mEh4cLDGzy5B0Y2yRnnDnPY8BwgPHDxxzcWixnJCQcnmGQVmx4gLHZ4kPFbR5z5gZmyZnb8DjsBlALj8HhxI0NjI03Z5y5zWPZcICNmZdILQ1Av5zjMTiQQKSW+QyMTUAtB4jQcuYBSEta4gZmRlAgJ/NIzjjYjN8vx3OSP/NU2CTOb29/CIxKO3t+/uaDHz7i0cLAwJMA0XsYLsLYgE89ELAfAFPyhNSNglEwCkbByAUAZlJayenjjYQAAAAASUVORK5CYII=","orcid":"","institution":"Xi’an University","correspondingAuthor":true,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"Hu","suffix":""},{"id":613576343,"identity":"43e23034-ea11-4eb2-9623-46ee6aa162a2","order_by":1,"name":"Xiaoli Zhang","email":"","orcid":"","institution":"Xi’an University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Zhang","suffix":""},{"id":613576344,"identity":"308cfa23-9290-4e7a-94e8-19d00a4b4a5b","order_by":2,"name":"Shaofei Dong","email":"","orcid":"","institution":"Xi’an University","correspondingAuthor":false,"prefix":"","firstName":"Shaofei","middleName":"","lastName":"Dong","suffix":""},{"id":613576345,"identity":"164b0c64-50d4-490d-a2a2-638ab1c4de3f","order_by":3,"name":"Zhifu Yin","email":"","orcid":"","institution":"Xi’an University","correspondingAuthor":false,"prefix":"","firstName":"Zhifu","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2026-03-17 07:25:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9145430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9145430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106976018,"identity":"7a53a722-aafd-4b9c-a76c-7d6ce8c6d773","added_by":"auto","created_at":"2026-04-15 10:43:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1274408,"visible":true,"origin":"","legend":"","description":"","filename":"uncertaintymanuscriptnpj.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9145430/v1_covered_eab22a80-f1d7-413b-a116-d9ab37e182b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Feature Sensitivity and Robustness in Corrosion Rate Forecasting: A Comparative Study of Deterministic and Probabilistic Machine Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Corrosion rate prediction, Extra Trees Regressor (ETR), Improved Bayesian Neural Network (ImprovedBNN), Uncertainty quantification, Feature sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-9145430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9145430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate corrosion rate prediction is critical for ensuring industrial infrastructure safety and reducing economic losses, yet existing models often fail to balance precision, uncertainty quantification, and adaptability to dynamic scenarios. This study systematically compared the Extra Trees Regressor (ETR) and an Improved Bayesian Neural Network (ImprovedBNN) across static performance metrics (MSE, MAE, coverage), feature sensitivity to ±10% fluctuations, and statistical robustness via paired t-tests. The results show that ETR outperformed in point prediction (MSE=0.0051) while ImprovedBNN achieved perfect uncertainty coverage (1.000) with enhanced sensitivity to critical features, with both models demonstrating distinct engineering applicability. These findings establish a framework for model selection in corrosion management, reconciling deterministic precision and probabilistic rigor to inform practical decision-making.\u003c/p\u003e","manuscriptTitle":"Feature Sensitivity and Robustness in Corrosion Rate Forecasting: A Comparative Study of Deterministic and Probabilistic Machine Learning Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 16:58:11","doi":"10.21203/rs.3.rs-9145430/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71f36399-2155-43c2-8e55-5d6a45277997","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65293840,"name":"Physical sciences/Engineering"},{"id":65293841,"name":"Physical sciences/Materials science"},{"id":65293842,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-15T10:42:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 16:58:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9145430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9145430","identity":"rs-9145430","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}