Spatial Data Mining for Prediction of Unobserved Zinc Pollutant using Various Kriging Methods

Spatial Data Mining for Prediction of Unobserved Zinc Pollutant using Various Kriging Methods | 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 Spatial Data Mining for Prediction of Unobserved Zinc Pollutant using Various Kriging Methods Durga pujitha Krotha, Fathimabi SK, JayaLakshmi G, Suneetha M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3770766/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 After years of contamination, rivers may get large amounts of heavy metal pollution. Our investigation's goal is to identify the river's hazardous locations. In our study case, we select the zinc-contaminated floodplains of the Meuse River (Zn). Excessive zinc levels may lead to a variety of health issues, including anemia, rashes, vomiting, and cramping in the stomach. However, there isn't a lot of sample data available about the Meuse River's zinc concentration; as a result, it's necessary to generate the missing data in unidentified regions. This study employs universal Kriging in spatial data mining to explore and predict unknown zinc pollutants. The semivariogram is a useful tool for representing the variability pattern of zinc. To predict the unknown regions, this captured model will be interpolated using the Kriging method. Regression with geographic weighting makes it possible to see how stimulus-response relationships change over space. We use a variety of semivariograms in our work, such as matern, exponential, and linear models. We also propose Universal Kriging and geographically weighted regression. The experimental findings show that: (i) the matern model, as determined by calculating the minimum error sum of squares, is the best theoretical semivariogram model; and (ii) the accuracy of the predictions can be visually demonstrated by projecting the results onto the real map. spatial data mining missing data semivariogram Universal Kriging Geographically weighted Regression 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. 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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-3770766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":262572484,"identity":"c0f75328-3603-4cd2-8f67-a126428be797","order_by":0,"name":"Durga pujitha Krotha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDCCAxCKmY2Bh4Eh4cc/ObDgA6K0sAG1POw5YAwWTCBCCwMDUAvjA7YDiQ0gDj4tfDdyH7/4ueMwO59878EPCTx30ueHHX4ItMVOTrcBuxbJG+lmlr1nDgMdxpcskWDxLHfj7TQDoJZkY7MD2LUY3EhjM+BtA2nhMZBI4GHO3Tg7AaTlQOI2PFoM/0K0GP9IYGNON5yd/oGQFubHUFvMJBLYDifIS+fgt0XyzDM2Ztkz6UAtOWYWiT1phhukcwoOJBjg9gvf8TTmj293WCfLN58xvvnjh428/Oz0zR8+VNjJ4dICBGwSjA0MyQinglUa4FQOAswfgFrs4Fz5BryqR8EoGAWjYAQCAPA6Y7RADA+2AAAAAElFTkSuQmCC","orcid":"","institution":"Jawaharlal Nehru Technological University, Kakinada","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Durga","middleName":"pujitha","lastName":"Krotha","suffix":""},{"id":262572486,"identity":"694216d9-b5e3-472e-b8d7-439a0a3cff0b","order_by":1,"name":"Fathimabi SK","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University, Kakinada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fathimabi","middleName":"","lastName":"SK","suffix":""},{"id":262572487,"identity":"5f600e9a-b009-4e13-91b3-f868dc602ec9","order_by":2,"name":"JayaLakshmi G","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University, Kakinada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"JayaLakshmi","middleName":"","lastName":"G","suffix":""},{"id":262572488,"identity":"629dce2b-d03d-4aa9-b245-f758965b5359","order_by":3,"name":"Suneetha M","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University, Kakinada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suneetha","middleName":"","lastName":"M","suffix":""}],"badges":[],"createdAt":"2023-12-18 08:14:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3770766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3770766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49119084,"identity":"71544613-8d18-46e5-b890-d54f38783408","added_by":"auto","created_at":"2024-01-03 12:07:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650587,"visible":true,"origin":"","legend":"","description":"","filename":"SpatialDataMiningforPredictionofUnobservedZincPollutantusingVariousKrigingMethods1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3770766/v1_covered_d7518a1d-ab47-4d49-9474-2469c1cd98aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Data Mining for Prediction of Unobserved Zinc Pollutant using Various Kriging Methods","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":"spatial data mining, missing data, semivariogram, Universal Kriging, Geographically weighted Regression","lastPublishedDoi":"10.21203/rs.3.rs-3770766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3770766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAfter years of contamination, rivers may get large amounts of heavy metal pollution. 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