Spatially Optimised Sensor Networks for Efficient Urban Temperature Monitoring and Prediction

Spatially Optimised Sensor Networks for Efficient Urban Temperature Monitoring and Prediction | 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 Spatially Optimised Sensor Networks for Efficient Urban Temperature Monitoring and Prediction Lidia L. Vitanova, Radomir Peev, Dessislava Petrova-Antonova, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8418685/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This research presents an integrated approach that combines a high-resolution Weather Research and Forecasting (WRF)-based experimental environment, a terrain- and land-cover-informed sensor placement algorithm, and a hybrid Random Forest–Gaussian Process model for mapping urban temperatures. It assesses the impact of the spatial arrangement of sensor stations on the reconstruction of temperature fields across complex urban terrains. The results indicate that static surface characteristics such as elevation and land use explain most of the daytime thermal variation and a significant portion at night. However, strategically placed, non-uniform stations are essential for capturing the remaining fine-scale gradients. An optimised network of around 200 sensors achieved mapping accuracy comparable to a uniformly distributed network exceeding 300 stations, proving that data-driven network optimisation can substantially reduce deployment costs. The benefits were most pronounced in topographically complex or thermally stable nocturnal conditions, where optimised layouts mitigated significant systematic errors. These outcomes emphasise that spatial intelligence in sensor placement can effectively substitute for dense measurements. However, a full representation of sub-grid processes still requires additional fine-scale meteorological inputs. The approach offers city planners a practical, open-source workflow for designing efficient temperature-monitoring networks that support resilient, data-informed, and climate-responsive urban development. climate prediction weather research and forecasting model (WRF) urban heat island (UHI) random forest–gaussian process model observation sensor networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 20 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 29 Dec, 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-8418685","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574811062,"identity":"2f0977ba-e952-4acc-b742-f6cb5e83449a","order_by":0,"name":"Lidia L. 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"climate prediction, weather research and forecasting model (WRF), urban heat island (UHI), random forest–gaussian process model, observation sensor networks","lastPublishedDoi":"10.21203/rs.3.rs-8418685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8418685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research presents an integrated approach that combines a high-resolution Weather Research and Forecasting (WRF)-based experimental environment, a terrain- and land-cover-informed sensor placement algorithm, and a hybrid Random Forest\u0026ndash;Gaussian Process model for mapping urban temperatures. It assesses the impact of the spatial arrangement of sensor stations on the reconstruction of temperature fields across complex urban terrains. The results indicate that static surface characteristics such as elevation and land use explain most of the daytime thermal variation and a significant portion at night. However, strategically placed, non-uniform stations are essential for capturing the remaining fine-scale gradients. An optimised network of around 200 sensors achieved mapping accuracy comparable to a uniformly distributed network exceeding 300 stations, proving that data-driven network optimisation can substantially reduce deployment costs. The benefits were most pronounced in topographically complex or thermally stable nocturnal conditions, where optimised layouts mitigated significant systematic errors. These outcomes emphasise that spatial intelligence in sensor placement can effectively substitute for dense measurements. However, a full representation of sub-grid processes still requires additional fine-scale meteorological inputs. The approach offers city planners a practical, open-source workflow for designing efficient temperature-monitoring networks that support resilient, data-informed, and climate-responsive urban development.\u003c/p\u003e","manuscriptTitle":"Spatially Optimised Sensor Networks for Efficient Urban Temperature Monitoring and Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 04:26:05","doi":"10.21203/rs.3.rs-8418685/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T08:47:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T10:53:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T11:28:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173330378042098120342839865131667753424","date":"2026-03-09T15:02:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41921984534522164894285689333288603618","date":"2026-03-09T12:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T05:00:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T13:03:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T11:21:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Cities","date":"2025-12-29T11:13:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-cities","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Cities](https://www.springer.com/journal/44327)","snPcode":"44327","submissionUrl":"https://submission.springernature.com/new-submission/44327/3","title":"Discover Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5e6a4cb-5f02-4fef-b8ec-adf607298340","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T06:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 04:26:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8418685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8418685","identity":"rs-8418685","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}