{"paper_id":"10012966-d4bf-4e1d-b2ef-1d3dfb990608","body_text":"Harnessing Machine Learning and Multi-source Data Fusion for Urban Fire Risk Assessment: Predictive Analysis of Spatial Heterogeneity | 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 Harnessing Machine Learning and Multi-source Data Fusion for Urban Fire Risk Assessment: Predictive Analysis of Spatial Heterogeneity Zongjia Zhang, Zixi Guo, Sijin Wu, Pan Tang, Songyi Wang, Lili Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7551994/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract As a giant system with many functional elements and complex structural layout, the occurrence of fire in megacities is influenced by multiple factors such as building type, regional planning, and socio-economic environment, resulting in a high uncertainty in the probability and risk of fire occurring in different areas of the city. This paper quantitatively analyzed the factors influencing urban fire risk by combining historical fire event data and the development characteristics of megacities. Based on the key features of fire risk domain knowledge, a model for Urban Area Fire Probability Prediction (UAFPP) was constructed using machine learning and multi-source data fusion technology. The quantitative relationship between the regional fire probability and the economic population, social activities, urban land use type was analyzed. The UAFPP model exhibits robust predictive performance for urban fires, evidenced by consistent R2 and RMSE values across a three-year real fire accident data validation period. This confirms the effectiveness of capturing the spatial characteristics of urban fire risk within the framework. Through a Shenzhen case study, we analyzed spatial patterns and predicted metropolitan fire probabilities. We quantified risk characteristics using multi-source data to provide actionable insights for mitigating urban fire risks. urban fires fire probability machine learning megacities risk analysis Full Text Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Major revisions 24 Nov, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 10 Sep, 2025 First submitted to journal 10 Sep, 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. 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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-7551994\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":529506221,\"identity\":\"dc7ef35f-b9b2-413b-82d5-095837a26bc4\",\"order_by\":0,\"name\":\"Zongjia Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jinan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zongjia\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":529506222,\"identity\":\"c346cd6d-7c50-4c2b-bb3e-e6925f31965d\",\"order_by\":1,\"name\":\"Zixi 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