STNN-based weighted regression models for the prediction of urban land surface temperature-A study of spatiotemporal association

STNN-based weighted regression models for the prediction of urban land surface temperature-A study of spatiotemporal association | 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 STNN-based weighted regression models for the prediction of urban land surface temperature-A study of spatiotemporal association Anwesha Sengupta, Asif Iqbal Middya, Sudipta Mondal, Sarbani Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7053407/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted 12 You are reading this latest preprint version Abstract In view of rapid environmental degradation, the city of Delhi faces prolonged heat waves and rising temperatures. Policymakers can leverage AI-based prediction models by utilizing remote sensing techniques to predict urban heat patterns, which enables data-driven decision-making for climate resilience. Earlier research shows a wide interdependency between LST (land surface temperature) and other risk factors, e.g., air pollutants, climatic, and land use characteristics variables. In this study, a significant positive correlation is observed between ozone and LST, whereas other pollutants show an opposite correlation with LST. This inverse relationship occurs because pollutants absorb solar radiation, limiting the amount of sunlight reaching the Earth’s surface, which in turn reduces the urban LST. However, the western and northwest regions of Delhi show consistently higher LST throughout the year of the study due to dense residential and commercial hubs. In addition, the present study aims to develop three STNN (Spatiotemporal Neural Network)-based weighted regression models (viz., STNNWR-v1, STNNWR-v2, and STNNWR-v3). Attention is paid to lowering the model complexity by reducing the number of parameters. The proposed models are compared with other traditional spatiotemporal models, which proves the performance of STNNWR-v3 is significantly better in terms of evolution metrics and computation time. The performance metrics for STNNWR-v3 for 30 independent trials are as follows: R2 score 0.931±0.003 and MSE 2.886±0.118(mean ± standard deviation). Moreover, execution time analysis further highlights that the computation time required for each independent trial is significantly less due to the smaller number of parameters used. Land surface temperature (LST) Spatiotemporal neural network-based weighted regression (STNNWR) Spatiotemporal association Urban air pollution Remote sensing application Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTAAC.pdf Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 02 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 12 Jul, 2025 Editor assigned by journal 06 Jul, 2025 Submission checks completed at journal 06 Jul, 2025 First submitted to journal 05 Jul, 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. 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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-7053407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492732171,"identity":"13f7d120-34d1-4603-bf96-8f74a8b8156e","order_by":0,"name":"Anwesha Sengupta","email":"","orcid":"","institution":"Jadavpur University","correspondingAuthor":false,"prefix":"","firstName":"Anwesha","middleName":"","lastName":"Sengupta","suffix":""},{"id":492732172,"identity":"8a11e02d-165f-4d9f-83a7-df6924a13d1b","order_by":1,"name":"Asif Iqbal 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