Assessing Climate Stress, Biodiversity and Urban Quality of Life in Île-de-France Using Artificial Intelligence and GIS

Assessing Climate Stress, Biodiversity and Urban Quality of Life in Île-de-France Using Artificial Intelligence and GIS | 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 Assessing Climate Stress, Biodiversity and Urban Quality of Life in Île-de-France Using Artificial Intelligence and GIS Sid Ahmed ZENAGUI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8775804/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 Urban areas are increasingly exposed to climate stress, particularly heat extremes, while simultaneously facing biodiversity loss and growing inequalities in quality of life. Understanding the spatial interactions among these dimensions is essential for designing effective and equitable urban adaptation strategies. This study develops an integrated AI–GIS analytical framework to assess climate stress, urban biodiversity, and quality of life across the Île-de-France metropolitan region. Using a combination of satellite-derived land surface temperature, extreme heat indicators, vegetation indices, landscape fragmentation metrics, and socio-environmental variables, the analysis captures spatial heterogeneity at the departmental level. Geographic information systems and spatial statistics are employed to identify heat stress hotspots and ecological connectivity patterns, while machine learning models (Random Forest, XGBoost, and artificial neural networks) are used to model nonlinear relationships and assess predictor importance. The results reveal pronounced intra-metropolitan disparities in climate stress, with densely urbanized departments experiencing higher land surface temperatures, more frequent heatwave exposure, and greater population vulnerability. Areas with lower vegetation cover and higher ecological fragmentation exhibit amplified thermal stress, whereas well-connected green infrastructures contribute to climate mitigation and improved quality-of-life outcomes. Artificial intelligence models confirm the critical role of vegetation indices, built-up density, and green space accessibility as key determinants of urban heat intensity. Integrated clustering further identifies distinct spatial typologies combining climate stress, biodiversity conditions, and quality-of-life indicators. Overall, the findings highlight the necessity of integrated urban planning approaches that simultaneously address climate adaptation, biodiversity conservation, and environmental equity. The proposed framework offers a transferable decision-support tool for climate-resilient metropolitan planning under ongoing climate change. JEL Classification · Q54 – Climate; Natural Disasters; Global Warming · Q57 – Ecological Economics; Ecosystem Services · R14 – Land Use Patterns; Urban Spatial Structure · R58 – Regional Development Policy · C45 – Neural Networks and Related Topics City Management and Urban Policy Urban heat stress Urban biodiversity Quality of life Artificial intelligence GIS-based spatial analysis 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-8775804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585038907,"identity":"9e50b8d8-ad56-4c06-baae-38eacf3718e5","order_by":0,"name":"Sid Ahmed ZENAGUI","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-5333-5106","institution":"University of Ain Temouchent ALGERIA","correspondingAuthor":true,"prefix":"","firstName":"Sid","middleName":"Ahmed","lastName":"ZENAGUI","suffix":""}],"badges":[],"createdAt":"2026-02-03 12:27:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8775804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8775804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101881719,"identity":"19d64f2e-93c7-4cd1-8a93-add5d19d1e02","added_by":"auto","created_at":"2026-02-04 15:15:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1196487,"visible":true,"origin":"","legend":"","description":"","filename":"AssessingClimateStress.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8775804/v1_covered_c4cf73c9-a631-4e38-ac78-8b11e1477d94.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing Climate Stress, Biodiversity and Urban Quality of Life in Île-de-France Using Artificial Intelligence and GIS\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Urban heat stress, Urban biodiversity, Quality of life, Artificial intelligence, GIS-based spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-8775804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8775804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban areas are increasingly exposed to climate stress, particularly heat extremes, while simultaneously facing biodiversity loss and growing inequalities in quality of life. Understanding the spatial interactions among these dimensions is essential for designing effective and equitable urban adaptation strategies. This study develops an integrated \u003cstrong\u003eAI–GIS analytical framework\u003c/strong\u003e to assess climate stress, urban biodiversity, and quality of life across the Île-de-France metropolitan region.\u003c/p\u003e\n\u003cp\u003eUsing a combination of satellite-derived land surface temperature, extreme heat indicators, vegetation indices, landscape fragmentation metrics, and socio-environmental variables, the analysis captures spatial heterogeneity at the departmental level. Geographic information systems and spatial statistics are employed to identify heat stress hotspots and ecological connectivity patterns, while machine learning models (Random Forest, XGBoost, and artificial neural networks) are used to model nonlinear relationships and assess predictor importance.\u003c/p\u003e\n\u003cp\u003eThe results reveal pronounced intra-metropolitan disparities in climate stress, with densely urbanized departments experiencing higher land surface temperatures, more frequent heatwave exposure, and greater population vulnerability. Areas with lower vegetation cover and higher ecological fragmentation exhibit amplified thermal stress, whereas well-connected green infrastructures contribute to climate mitigation and improved quality-of-life outcomes. Artificial intelligence models confirm the critical role of vegetation indices, built-up density, and green space accessibility as key determinants of urban heat intensity. 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