Landslide susceptibility assessment using hybrid integration of best-first decision tree and machine learning ensembles

Landslide susceptibility assessment using hybrid integration of best-first decision tree and machine learning ensembles | 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 Landslide susceptibility assessment using hybrid integration of best-first decision tree and machine learning ensembles Jianguo Wang, Weipeng Li, Linhai Li, Yuchao Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4771084/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 During the study, we investigate and compare spatial prediction result of landslide hazards with a relative less-used model BFT (Best-first Decision Tree) and its three integrated models RSBFT (RandomSubspace ensemble based BFTree), MBBFT (MultiBoost ensemble based BFT), BABFT (Bagging ensemble based BFT) in Meixian County, Baoji city, Shaanxi province, China. BFTree is a machine learning technique by optimizing split nodes of standard decision tree. Integrated learning is an excellent method by combining several weakly supervised models into a strong supervised model. For data preparation, 87 historical landslide events as landslide inven-tory map and 16 landslide conditioning factors as spatial database have been collected and organized in the study area. At last, the FR (frequency ratio) method was applied for the correlation analysis and CAE (correla-tion attribute evaluation) method was applied for analyzing contribution value of each factor. For the model studies, landslide susceptibility indexes would be possible to measure using BFT, BABFT, MBBFT, RSBFT models and prepared data. Then, four landslide susceptibility maps are generated. At last, randomly assigned 61 (70%) landslides locations has been used to build the landslide models. The other 26 (30%) landslide loca-tions were used to validate. The result of verification shows that three ensemble models have boosted the pre-dictive ability of the base model; MBBFT have better prediction ability than others; RSBFT model has no overfitting problems. landslide susceptibility machine learning frequency ratio Best-first decision tree 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-4771084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338862519,"identity":"f19b9a92-ebb8-4bf2-9028-4fae9cf89269","order_by":0,"name":"Jianguo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDADfmbmww9I0yLZzpZmQJoWg/M8ChJEqZSfkZ264eOeusTNh3kYDBhqbKIJG34jd9vNGc/YjM0O8x54wHAsLbeBoBaJ3G23eQ7wyJkd5kswYGw4TFiL/Ayglj8HJHiMm3kMJIjSwgB02G2GAwZyBszEajE483bbzZ4DCcYSh4GBnECMX+Tbc7fd+HGgLrG///DhBx9qbIhwGApIIE35KBgFo2AUjAJcAABRLD+iXAfgDQAAAABJRU5ErkJggg==","orcid":"","institution":"Guoneng Xinjiang Tokson Energy Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Wang","suffix":""},{"id":338862521,"identity":"03b5c7b5-f615-4364-920f-b9e318a0dc36","order_by":1,"name":"Weipeng Li","email":"","orcid":"","institution":"Guoneng Xinjiang Tokson Energy Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Weipeng","middleName":"","lastName":"Li","suffix":""},{"id":338862522,"identity":"c80f130c-9aaa-4c21-a6a1-a361885cc548","order_by":2,"name":"Linhai Li","email":"","orcid":"","institution":"Guoneng Xinjiang Tokson Energy Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Linhai","middleName":"","lastName":"Li","suffix":""},{"id":338862524,"identity":"079566bd-61f9-4896-8418-0eb0e6ccd68b","order_by":3,"name":"Yuchao Fan","email":"","orcid":"","institution":"Guoneng Xinjiang Tokson Energy Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yuchao","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-07-20 03:54:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4771084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4771084/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64337426,"identity":"d4bb499f-37c7-407d-ab78-09820f6be745","added_by":"auto","created_at":"2024-09-11 23:08:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2576741,"visible":true,"origin":"","legend":"","description":"","filename":"Landslidesusceptibilityassessmentusinghybridintegrationofbestfirstdecisiontreeandmachinelearningensembles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4771084/v1_covered_e5c27b3f-744f-4b04-a3b0-2974a1e43885.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Landslide susceptibility assessment using hybrid integration of best-first decision tree and machine learning ensembles","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":"landslide susceptibility, machine learning, frequency ratio, Best-first decision tree","lastPublishedDoi":"10.21203/rs.3.rs-4771084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4771084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDuring the study, we investigate and compare spatial prediction result of landslide hazards with a relative less-used model BFT (Best-first Decision Tree) and its three integrated models RSBFT (RandomSubspace ensemble based BFTree), MBBFT (MultiBoost ensemble based BFT), BABFT (Bagging ensemble based BFT) in Meixian County, Baoji city, Shaanxi province, China. BFTree is a machine learning technique by optimizing split nodes of standard decision tree. Integrated learning is an excellent method by combining several weakly supervised models into a strong supervised model. For data preparation, 87 historical landslide events as landslide inven-tory map and 16 landslide conditioning factors as spatial database have been collected and organized in the study area. At last, the FR (frequency ratio) method was applied for the correlation analysis and CAE (correla-tion attribute evaluation) method was applied for analyzing contribution value of each factor. For the model studies, landslide susceptibility indexes would be possible to measure using BFT, BABFT, MBBFT, RSBFT models and prepared data. Then, four landslide susceptibility maps are generated. At last, randomly assigned 61 (70%) landslides locations has been used to build the landslide models. The other 26 (30%) landslide loca-tions were used to validate. The result of verification shows that three ensemble models have boosted the pre-dictive ability of the base model; MBBFT have better prediction ability than others; RSBFT model has no overfitting problems.\u003c/p\u003e","manuscriptTitle":"Landslide susceptibility assessment using hybrid integration of best-first decision tree and machine learning ensembles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 13:57:30","doi":"10.21203/rs.3.rs-4771084/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":"ae4bd9ce-c726-428f-8171-ae0ac461b5bc","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T23:00:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-22 13:57:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4771084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4771084","identity":"rs-4771084","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}