Assessing Seasonal Fluctuations in Forecast Precision through Comparative Regression Modelling in Meteorology

Assessing Seasonal Fluctuations in Forecast Precision through Comparative Regression Modelling in Meteorology | 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 Seasonal Fluctuations in Forecast Precision through Comparative Regression Modelling in Meteorology Shravankumar Masalvad, Vartika Paliwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5397718/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 This study provides an in-depth review of various regression models used to forecast meteorological parameters across seasons. Regression models that use traditional regression can be evaluated against advanced machine learning techniques like Random Forest and Gradient Boosting to evaluate their predictive power using metrics such as root mean square Error (RMSE), Mean Absolute Error (MAE) as well as Mean Absolute Percentage Error (MAPE) to calculate R 2 and ratio between RSR/RMSE to observer Standard Deviation ratio, Kling-Gupta Efficiency (KGE). The research highlights notable performance differences over time, highlighting both the variability of weather data as well as the challenges associated with accurate forecasting. The Ridge Regression model stands out from other models with one of the most accurate error metrics (RMSE: 294.87, MAE: 232.58, MAPE 7.74 RSR = 0.81); as well as consistently producing R 2 values of 0.34 and KGE values of 0.53 within its model parameters. The methods adopted in this research would help the stakeholders, civic bodies and others for attaining sustainable water resources approach to tackle the repercussions of climate change. Seasonal Predictive Modeling Meteorological Forecasting Regression Analysis Machine Learning Algorithms Performance Metrics Weather Data Variability 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. 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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-5397718","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380583446,"identity":"f0a225da-8b48-47df-a2b6-ae3ca4c9a91c","order_by":0,"name":"Shravankumar Masalvad","email":"data:image/png;base64,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","orcid":"","institution":"Sreenidhi Institute of Science and Technology Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Shravankumar","middleName":"","lastName":"Masalvad","suffix":""},{"id":380583450,"identity":"7c4af88b-8cf8-4ddf-b559-20071980ce5d","order_by":1,"name":"Vartika Paliwal","email":"","orcid":"","institution":"genba moze college of engineering","correspondingAuthor":false,"prefix":"","firstName":"Vartika","middleName":"","lastName":"Paliwal","suffix":""}],"badges":[],"createdAt":"2024-11-05 19:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5397718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397718/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71457276,"identity":"31ebb2e6-e439-4ea3-a134-2d84a93a4888","added_by":"auto","created_at":"2024-12-15 22:01:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1036815,"visible":true,"origin":"","legend":"","description":"","filename":"SeasonalVariationsinPredictiveAccuracy7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397718/v1_covered_ea223e05-ac1e-4b3c-8c14-7dcd1dc4bb30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Seasonal Fluctuations in Forecast Precision through Comparative Regression Modelling in Meteorology","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":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":"Seasonal Predictive Modeling, Meteorological Forecasting, Regression Analysis, Machine Learning Algorithms, Performance Metrics, Weather Data Variability","lastPublishedDoi":"10.21203/rs.3.rs-5397718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5397718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study provides an in-depth review of various regression models used to forecast meteorological parameters across seasons. 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