Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic

Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic | 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 Method Article Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic Shi-Shun Chen, Xiao-Yang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6296606/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 Global sensitivity analysis (GSA) can provide rich information for controlling output uncertainty. In practical applications, segmented models are commonly used to describe an abrupt model change. For segmented models, the complicated uncertainty propagation during the transition region may lead to different importance rankings of different GSA methods. If an unsuitable GSA method is applied, misleading results will be obtained, resulting in suboptimal or even wrong decisions. In this paper, four GSA indices, i.e., Sobol index, mutual information, delta index and PAWN index, are applied for a segmented fire spread model (Dry Eucalypt). The results show that four GSA indices give different importance rankings during the transition region since segmented characteristics affect different GSA indices in different ways. We suggest that analysts should rely on the results of different GSA indices according to their practical purpose, especially when making decisions for segmented models during the transition region. All of our source codes are publicly available at https://github.com/dirge1/GSA_segmented . Systems Engineering Industrial Engineering Environmental Engineering Global sensitivity analysis Piecewise model Variance-based method Moment-independent method Decision making Full Text Additional Declarations The authors declare no competing interests. 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-6296606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":433267790,"identity":"53f1a855-4660-4ee7-a4b2-a148cda64c76","order_by":0,"name":"Shi-Shun Chen","email":"","orcid":"https://orcid.org/0000-0003-0590-7114","institution":"School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China","correspondingAuthor":false,"prefix":"","firstName":"Shi-Shun","middleName":"","lastName":"Chen","suffix":""},{"id":433267791,"identity":"9ebe6cc6-fc55-4d09-86de-cbd25a8a198b","order_by":1,"name":"Xiao-Yang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPjBZcQDC4yFGCxuYPEOyFsY2krSwNx+T5p13J7G//QDjg7dtDPLmBLXwHEs25t32LHHGmQRmw7ltDIY7GwhpkcgxfMy77XBiw4EENmneNoYEgwOEtMi//3CYd87hxPnnH7D/Jk6LBA/jY96Gw4kbbiSwMROnhSfN2HDOsWfGG288bJacc07CcAMhLfzsh59JvKm5IzvvfPLBD2/KbOQJ2gICTJDoYGwAEhJEqAep/UGculEwCkbBKBipAABZD0EpWWQRqwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4046-2934","institution":"School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China","correspondingAuthor":true,"prefix":"","firstName":"Xiao-Yang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-24 14:59:00","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6296606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6296606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79149397,"identity":"942a449b-2af6-40f6-9adb-b2fabe165049","added_by":"auto","created_at":"2025-03-25 03:59:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329683,"visible":true,"origin":"","legend":"","description":"","filename":"ComparisonofglobalsensitivityanalysismethodsR1print.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6296606/v1_covered_e98f44ed-6999-4488-9963-50de38924eb7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Beihang University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Global sensitivity analysis, Piecewise model, Variance-based method, Moment-independent method, Decision making","lastPublishedDoi":"10.21203/rs.3.rs-6296606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6296606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal sensitivity analysis (GSA) can provide rich information for controlling output uncertainty. 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