Stacking model based on six base classifiers to improve prediction of soil liquefaction: a multi-dataset study

Stacking model based on six base classifiers to improve prediction of soil liquefaction: a multi-dataset study | 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 Research Article Stacking model based on six base classifiers to improve prediction of soil liquefaction: a multi-dataset study Xiaofei Yao, Yumin Chen, Hongmei Gao, Saeed Sarajpoor, zhenxiong Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4325165/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 Prediction of soil liquefaction during earthquake is a crucial task to mitigate or avoid damage caused by liquefaction. The existing machine learning methods have achieved satisfactory prediction accuracy on specific datasets, but they are unable to perform well on other datasets. To overcome the limitation, a novel prediction method based on stacking strategy are proposed to evaluate earthquake-induced liquefaction potential of soil, which is composed of six base classifiers and secondary classifier. The hyperparameters are tuned by grid search algorithm and the AUC value under ten folds cross validation are utilized as the basis for obtain the optimal hyperparameters. The applicability of stacking model was verified using three widely used datasets. Six performance metrics are utilized to analyze and compare the performance of base classifiers and stacking model. The result indicates proposed model outperforms base classifier in all three datasets in terms of the metrics mentioned above. Furthermore, the proposed method underwent a comparative evaluation against other existing machine learning techniques, revealing that the prediction accuracy achieved by the proposed model surpasses that of the existing methods. Also, this study investigated the importance of input parameters so as to interpret the complicated relationship between liquefaction potential and input parameters. Soil liquefaction Machine learning Stacking model Liquefaction potential Grid search Hyperparameters Full Text 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. 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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-4325165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336088473,"identity":"ea1178fa-28b8-44b5-9621-761caaf6bfd9","order_by":0,"name":"Xiaofei Yao","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Yao","suffix":""},{"id":336088474,"identity":"6d2748d4-f412-4393-b692-8e0c347e1698","order_by":1,"name":"Yumin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYHACNoaPDQdADAPitTDOJFkLMy9JWgyuHX722HbHncQG9uZtEgw1d4jQcjvN3Dj3zLPEBp5jZRIMx54RoyWHTTq37XBig0SOmQRjw2EitViCtMi/IUULI9gWHiK1SN5OM5PsbTts3MaTVmyRcIwILXy3k59J/Gw7LNvPfnjjjQ81RGhROABlsIGIBMIaGBjkG4hRNQpGwSgYBSMbAAAZMjwMGdT/SgAAAABJRU5ErkJggg==","orcid":"","institution":"Hohai University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yumin","middleName":"","lastName":"Chen","suffix":""},{"id":336088475,"identity":"d1333b1f-3cad-4ded-9a30-3cf8d98ef8c3","order_by":2,"name":"Hongmei Gao","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Gao","suffix":""},{"id":336088476,"identity":"62cde82f-2bd3-4e2e-8f21-aa51dcd2adbe","order_by":3,"name":"Saeed Sarajpoor","email":"","orcid":"","institution":"Institute for small city of Chongqing university in Liyang","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Sarajpoor","suffix":""},{"id":336088477,"identity":"d7325550-ceba-4c90-916a-46686f794f27","order_by":4,"name":"zhenxiong Li","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zhenxiong","middleName":"","lastName":"Li","suffix":""},{"id":336088478,"identity":"f87adc7d-a8da-4c69-bae8-f205b4636ed7","order_by":5,"name":"Yi Han","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-04-25 15:41:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4325165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4325165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68851817,"identity":"cc90b515-71c6-41f9-9957-5b591d9821c1","added_by":"auto","created_at":"2024-11-12 17:29:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":862573,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4325165/v1_covered_276fbd44-275a-4b59-aca8-44192b5be707.pdf"}],"financialInterests":"","formattedTitle":"Stacking model based on six base classifiers to improve prediction of soil liquefaction: a multi-dataset study","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":"Soil liquefaction, Machine learning, Stacking model, Liquefaction potential, Grid search, Hyperparameters","lastPublishedDoi":"10.21203/rs.3.rs-4325165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4325165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrediction of soil liquefaction during earthquake is a crucial task to mitigate or avoid damage caused by liquefaction. The existing machine learning methods have achieved satisfactory prediction accuracy on specific datasets, but they are unable to perform well on other datasets. To overcome the limitation, a novel prediction method based on stacking strategy are proposed to evaluate earthquake-induced liquefaction potential of soil, which is composed of six base classifiers and secondary classifier. The hyperparameters are tuned by grid search algorithm and the AUC value under ten folds cross validation are utilized as the basis for obtain the optimal hyperparameters. The applicability of stacking model was verified using three widely used datasets. Six performance metrics are utilized to analyze and compare the performance of base classifiers and stacking model. The result indicates proposed model outperforms base classifier in all three datasets in terms of the metrics mentioned above. 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