Environmental Impact Prediction

Environmental Impact 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Article Environmental Impact Prediction Vivaan Kaundinya Vivaan, Abhijeet Kant Kumar Abhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5419185/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 Predicting environmental impacts is crucial for evaluating and reducing how human activity affects ecosystems, particularly when it comes to pollution, resource depletion, and climate change. In order to predict changes in the environment, this study looks at a variety of predictive models, including both sophisticated machine learning techniques and conventional statistical methods. We provide an integrated framework that increases prediction accuracy by utilizing data from several sources, including satellite images, climate records, air and water quality indexes, and socioeconomic characteristics. Using machine learning models, the study shows improved performance in short-term predictions of water pollution, soil deterioration, and air quality. However, due to data limitations and the complexity of ecological systems, long-term, complicated predictions—like those regarding habitat degradation and biodiversity loss—remain difficult. According to our research, enhancing future environmental effect projections and facilitating more sustainable decision-making processes would need incorporating real-time data, growing datasets, and creating adaptive models. Climate change ecosystem deterioration air quality soil degradation biodiversity loss satellite images data integration sustainability real-time data machine learning and environmental impact prediction 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. 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-5419185","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377769006,"identity":"5d944aa9-0dbe-400e-b11f-b8fbd75a517e","order_by":0,"name":"Vivaan Kaundinya Vivaan","email":"data:image/png;base64,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","orcid":"","institution":"Chandigarh University","correspondingAuthor":true,"prefix":"","firstName":"Vivaan","middleName":"Kaundinya","lastName":"Vivaan","suffix":""},{"id":377769007,"identity":"59ae6400-7c18-4037-b30f-95962f7d6c25","order_by":1,"name":"Abhijeet Kant Kumar Abhi","email":"","orcid":"","institution":"Chandigarh University","correspondingAuthor":false,"prefix":"","firstName":"Abhijeet","middleName":"Kant Kumar","lastName":"Abhi","suffix":""}],"badges":[],"createdAt":"2024-11-09 01:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5419185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5419185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72835739,"identity":"c24f6fac-e2ee-4f35-85bd-0842a57a347b","added_by":"auto","created_at":"2025-01-02 16:46:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":299390,"visible":true,"origin":"","legend":"","description":"","filename":"EnvironmentalImpactPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5419185/v1_covered_558a278f-3dd5-43e6-b354-4775595e7f7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Impact Prediction","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":"Climate change, ecosystem deterioration, air quality, soil degradation, biodiversity loss, satellite images, data integration, sustainability, real-time data, machine learning, and environmental impact prediction","lastPublishedDoi":"10.21203/rs.3.rs-5419185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5419185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting environmental impacts is crucial for evaluating and reducing how human activity affects ecosystems, particularly when it comes to pollution, resource depletion, and climate change. 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