Deduction of sudden rainstorm scenarios: Integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory

Deduction of sudden rainstorm scenarios: Integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory | 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 Deduction of sudden rainstorm scenarios: Integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory Xie Xiaoliang, 宇章 田, Wei Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2037954/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Event scenarios serve as the basis for emergency decision-making after sudden disasters, and the accuracy of scenario deduction directly determines the effectiveness of emergency management implementation. On July 20, 2021 an exceptionally heavy rainstorm disaster occurred in Zhengzhou, Henan Province, China causing serious urban waterlogging, river floods, flash floods and landslides, and resulting in major casualties and property losses:14.79 million people affected, 398 people killed or missing (380 people in Zhengzhou) and a direct economic loss of 120.06 billion RMB. In order to investigate the complex evolution process of this disaster, a dynamic Bayesian network, evidence theory and emotion update mechanism are integrated to develop an efficient and effective scenario deduction model, with an emphasis on combining subjective and objective factors. In this model, more attention is given to subjective factors such as decision makers' emotions. The elements of scenario deduction are classified into the situation status, meteorological factor , emergency activities, decision makers' emotions and emergency goals, the coupling relationship between the elements are comprehensively analyzed, and the influence of these elements on the evolution mechanism of the rainstorm disaster is investigated, so as to facilitate targeted emergency management measures for the rescue operations. The empirical results show that the proposed dynamic Bayesian network can effectively simulate the dynamic change process of scenario deduction, the improved Dempster-Shafer (DS) evidence theory can reduce the subjectivity of the model in dealing with the uncertainty of the evolution process, and the emotion update mechanism can adequately quantify and decrease the influence caused by the emotional changes of decision-makers. The model may better replicate actual events, and it may apply to the scenario deduction of other disasters, making an impact on the study of sudden catastrophes. dynamic Bayesian Network Scenario Deduction Scenario Element Improved DS Evidence Theory Sentiment Update Mechanism Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 Oct, 2022 Editor invited by journal 11 Oct, 2022 Editor assigned by journal 08 Sep, 2022 First submitted to journal 07 Sep, 2022 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-2037954","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":143636920,"identity":"41504f89-cb59-4b76-829c-978ff7573910","order_by":0,"name":"Xie Xiaoliang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Xiaoliang","suffix":""},{"id":143636921,"identity":"12b26468-5999-4c79-b26a-7d8d2172e250","order_by":1,"name":"宇章 田","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACZgiVACIOfjD4L8fG3n4Arw4eJC2MjyUqmI35eM4k4NfCgNDCbMBzhjlxnoSDAV4t9uzMzx7zttXl8c9uvyYh2caW3iYB1P+jYhseh7GZG/O2sRVL3DlTJlHYxpPbJt14gLHnzG18fjGT5m3jSWy4kZMGtEUit03mQAIzYxs+LezfgFokEueDtPC2GaSzSSQYENDCA7LFIHHDjfTDQO8nJBDWcpinTHLOuYTEjTdyQIF8wLANqO8gPr+w9x/fJvGmrC5x3o30B8CoPCAv395+8MGPCtxaQIAJEjk8iOg4gFc9EDD+gFj4gJDCUTAKRsEoGKEAAHBOVL0aB6SGAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0559-5985","institution":"Hunan University Business School","correspondingAuthor":true,"prefix":"","firstName":"宇章","middleName":"","lastName":"田","suffix":""},{"id":143636922,"identity":"7e352c9b-d9fe-4c71-ac1c-400456b0a044","order_by":2,"name":"Wei Guo","email":"","orcid":"","institution":"University of North Carolina at Pembroke University Libraries: UNC Pembroke Mary Livermore Library","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2022-09-06 13:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2037954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2037954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27867756,"identity":"eded5b8d-5007-4e7d-a50f-11224783a0d3","added_by":"auto","created_at":"2022-10-17 14:36:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830779,"visible":true,"origin":"","legend":"","description":"","filename":"Deductionofsuddenrainstormscenarios.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2037954/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Deduction of sudden rainstorm scenarios: Integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-2037954/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"dynamic Bayesian Network, Scenario Deduction, Scenario Element, Improved DS Evidence Theory, Sentiment Update Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-2037954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2037954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Event scenarios serve as the basis for emergency decision-making after sudden disasters, and the accuracy of scenario deduction directly determines the effectiveness of emergency management implementation. 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