Interpretable Machine Learning for Urban Fire Hazard Assessment with POI Integration

Interpretable Machine Learning for Urban Fire Hazard Assessment with POI Integration | 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 Article Interpretable Machine Learning for Urban Fire Hazard Assessment with POI Integration Xiaodong Zhou, Miaoxuan Shan, Chunlin Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6911285/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 With the rapid advancement of urbanization, the frequency of urban fire incidents has been steadily increasing, posing significant challenges to public safety and sustainable urban development. Understanding the complex relationships between various urban built environments is essential for improving fire risk assessment and management. In this study, we propose an interpretable machine learning framework that incorporates Point of Interests (POIs) to enhance the accuracy and transparency of urban fire risk prediction. Using POI data from Hangzhou, a major megacity in China, we constructed and optimized three machine learning models. Among them, the Light Gradient Boosting Machine (LightGBM) demonstrated the highest prediction accuracy. To address the interpretability of the model, we employed SHapley Additive exPlanations (SHAP) to analyze the contributions of different POI types to fire incident prediction. Our findings reveal that the density of Residential areas and Life Service facilities exerts the most significant influence on fire incidence, suggesting a strong correlation between fire risk, population density, and economic activity. Moreover, interaction effects between different POI types further contribute to the complexity of urban fire risk, indicating the need for integrated and context-specific prevention strategies. This study provides a transparent, data-driven approach to assessing urban fire risk, offering valuable insights for urban planners, policymakers, and emergency service managers. By elucidating the spatial distribution patterns of fire risk in relation to POI density, our model supports more informed decision-making aimed at enhancing urban resilience and sustainability. Earth and environmental sciences/Natural hazards Physical sciences/Mathematics and computing/Computer science Machine learning LightGBM Model interpretability Urban fire risk 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-6911285","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478214771,"identity":"669be955-fbe9-45a0-9872-32f7ab72a730","order_by":0,"name":"Xiaodong Zhou","email":"","orcid":"","institution":"People’s Public Security University of China","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Zhou","suffix":""},{"id":478214772,"identity":"6475457f-1027-4f5e-b2c2-6dfd14d71dcd","order_by":1,"name":"Miaoxuan Shan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYFAD9sbGhx9I08JzuNlYgjQtEultAjzEKJSfdsaAuXBHXWL/zIdtDBIMdnK6DQS0GNxOS2CeeeZw4ozbiW0PChiSjc0OENIinXyAmbftQOIG6cR2AwmGA4nbCGmRn53YANRSl7hB8mCbBA8xWhhug21hTtwgwUikFphfjGecSQQGsgERfpGfnQMOMdn+9uMPH36osJMjqAUI2H8zNsAtJawcDJgRWkbBKBgFo2AUYAEABRRBV2yBjl0AAAAASUVORK5CYII=","orcid":"","institution":"People’s Public Security University of China","correspondingAuthor":true,"prefix":"","firstName":"Miaoxuan","middleName":"","lastName":"Shan","suffix":""},{"id":478214773,"identity":"b4aa1f7a-c4f1-437d-a863-060e6c21f5c9","order_by":2,"name":"Chunlin Ye","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-06-17 07:08:32","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-6911285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6911285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89352149,"identity":"548be246-8ef0-4beb-8d06-d6a0d6452b8e","added_by":"auto","created_at":"2025-08-19 06:31:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1236416,"visible":true,"origin":"","legend":"","description":"","filename":"InterpretableMachineLearningforUrbanFireHazardAssessmentwithPOIIntegration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6911285/v1_covered_fc6f4c06-c0fa-417b-8b15-af935f7631c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interpretable Machine Learning for Urban Fire Hazard Assessment with POI Integration","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":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":"Machine learning, LightGBM, Model interpretability, Urban fire risk","lastPublishedDoi":"10.21203/rs.3.rs-6911285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6911285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid advancement of urbanization, the frequency of urban fire incidents has been steadily increasing, posing significant challenges to public safety and sustainable urban development. Understanding the complex relationships between various urban built environments is essential for improving fire risk assessment and management. In this study, we propose an interpretable machine learning framework that incorporates Point of Interests (POIs) to enhance the accuracy and transparency of urban fire risk prediction. Using POI data from Hangzhou, a major megacity in China, we constructed and optimized three machine learning models. Among them, the Light Gradient Boosting Machine (LightGBM) demonstrated the highest prediction accuracy. To address the interpretability of the model, we employed SHapley Additive exPlanations (SHAP) to analyze the contributions of different POI types to fire incident prediction. Our findings reveal that the density of Residential areas and Life Service facilities exerts the most significant influence on fire incidence, suggesting a strong correlation between fire risk, population density, and economic activity. Moreover, interaction effects between different POI types further contribute to the complexity of urban fire risk, indicating the need for integrated and context-specific prevention strategies. This study provides a transparent, data-driven approach to assessing urban fire risk, offering valuable insights for urban planners, policymakers, and emergency service managers. 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