{"paper_id":"1f987bc4-7e83-40fe-b9c0-dcbaa8970491","body_text":"Evaluation and attribution of flood disaster loss risk based on Bayesian network structure learning: A Case Study of the Urban Agglomeration in the Middle Yangtze River. | 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 Evaluation and attribution of flood disaster loss risk based on Bayesian network structure learning: A Case Study of the Urban Agglomeration in the Middle Yangtze River. Yang Zhixiang, Hanping Zhao, Kou Longbin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635536/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 Flood disaster constitutes a major threat to urban safety, and identifying and quantifying the causal relationships between flood disaster loss and influencing factors is critical for disaster prevention and mitigation strategies. In this study, a flood disaster loss risk evaluation and attribution analysis method based on the Bayesian Network structure learning algorithm is constructed. Taking the urban agglomeration in the middle Yangtze River as the study area, the risk levels of different cities and their uncertainties are assessed, the causal relationships between disaster-inducing factors, disaster-pregnant environments, disaster- affected bodies, and disaster loss risks are derived, and the significance of the factors affecting different risk levels is analyzed. The results show that the flood disaster loss risk is higher in the Poyang Lake and Dongting Lake basins, and the risk level and its uncertainty had the same trend in general; however, there were certain exceptional area that need attention, such as Nanchang and Xiangtan with high risk and low uncertainty, Wuhan and Xiangyang with low risk and high uncertainty; moreover, the main impacting factors of different risk levels were different, e.g., the main factors for level 1 were cumulative rainfall, river density and elevation standard deviation, while the main factors for level 2 were population density, cumulative rainfall, and urbanization. Flood disaster loss risk evaluation Attribution analysis Bayesian Network structure learning Urban agglomeration in the middle Yangtze River Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Jun, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 12 May, 2025 First submitted to journal 10 May, 2025 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-6635536\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":456819422,\"identity\":\"159032dc-e4ad-4a2b-bb14-3c1c21ec692e\",\"order_by\":0,\"name\":\"Yang Zhixiang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yang\",\"middleName\":\"\",\"lastName\":\"Zhixiang\",\"suffix\":\"\"},{\"id\":456819423,\"identity\":\"47d3ce69-fad4-4c4f-b15a-621dc95b099e\",\"order_by\":1,\"name\":\"Hanping Zhao\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACNvbG5gcfKg6AORJEaeHnOdxmOOMMKVokZ7g3SHO2kaLF4AZjgzHjvDt5BgeYD97mYbDLI6zldmPD48Jtz4oNDrAlW/MwJBcT1nLnYIPxzG2HEzcc4DGT5mE4kNhA2GGJDdK8c0Ba+L8Rp0VyBkhLA9gWNuK08PMcBAbysWeJMw+zGVvOMUgmrIWNvf3xgw81dxL7jjc/vPGmwo6wFgRgBhEGxKsfBaNgFIyCUYAHAAAg3UROBAGsIQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Beijing Normal University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hanping\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":456819424,\"identity\":\"75d784e1-4417-4703-9dba-f9bfe4ade6e4\",\"order_by\":2,\"name\":\"Kou Longbin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kou\",\"middleName\":\"\",\"lastName\":\"Longbin\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-10 15:05:28\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6635536/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6635536/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83030213,\"identity\":\"91cfc151-82a5-4bb1-b090-0ba552c53d64\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 08:57:34\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1526111,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Evaluationandattributionofflooddisasterlossrisk2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635536/v1_covered_36bc1f12-66b6-4ba5-bf55-41ea5c2576c2.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Evaluation and attribution of flood disaster loss risk based on Bayesian network structure learning: A Case Study of the Urban Agglomeration in the Middle Yangtze River.\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Flood disaster loss risk evaluation, Attribution analysis, Bayesian Network structure learning, Urban agglomeration in the middle Yangtze River\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6635536/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6635536/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFlood disaster constitutes a major threat to urban safety, and identifying and quantifying the causal relationships between flood disaster loss and influencing factors is critical for disaster prevention and mitigation strategies. 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