Analysis of Water Quality Using Machine Learning Techniques

Analysis of Water Quality Using Machine Learning Techniques | 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 Analysis of Water Quality Using Machine Learning Techniques Sujata Negi Thakur, Loveneet Singh, Nitin Koranga, Sandeep Singh Mehta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6452922/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 The research aims to analyse water quality using Python to determine its potability. In this context, machine learning models: XGBoost and Random Forest, achieving accuracies of up to 80% and 78%, respectively. The process begins by feeding the mod- els a dataset containing various water quality metrics from multiple sources. These models are then trained to predict water potability, containing parameters like phys- ical, chemical and biological. which is crucial for ensuring safe drinking water. The XGBoost model, which is most noted for its great efficiency and efficacy in classifying issues, can handle large datasets that include a lot of varying variables and is there- fore suitable for complicated water quality datasets. On the other hand, the Random Forest model is a robust ensemble technique that can provide higher accuracy through decision tree aggregation, thus enhancing predictive performance. The models are trained to recognize patterns and correlations within the data, enabling them to predict the quality of water with a certain level of confidence. Both models are instrumental in predicting water quality, and their deployment can sig- nificantly aid in monitoring and managing water resources effectively. The Python language, with its extensive libraries and tools, facilitates the implementation and optimization of these models, ensuring a scalable and reliable water quality analysis framework. The success of such a project could lead to improved water management practices and better health outcomes for communities relying on these water sources. The success of these models in water quality analysis underscores the potential of machine learning in environmental monitoring and protection efforts. Water Quality Analysis XGB Random forest Machine learning Deep learning Feature Extraction Full Text 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-6452922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452179950,"identity":"bea82737-5784-41c4-9c76-bc362d83ecfe","order_by":0,"name":"Sujata Negi Thakur","email":"data:image/png;base64,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","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":true,"prefix":"","firstName":"Sujata","middleName":"Negi","lastName":"Thakur","suffix":""},{"id":452179951,"identity":"1701e1e5-9993-43c7-a6a6-09c8e9340ccc","order_by":1,"name":"Loveneet Singh","email":"","orcid":"","institution":"GEHU: Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"Loveneet","middleName":"","lastName":"Singh","suffix":""},{"id":452179952,"identity":"ce84f693-058b-4788-970c-54b33a9cc9af","order_by":2,"name":"Nitin Koranga","email":"","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"","lastName":"Koranga","suffix":""},{"id":452179953,"identity":"5a13c7a1-1e8f-4bc9-8a21-df67a2a5db3a","order_by":3,"name":"Sandeep Singh Mehta","email":"","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"Singh","lastName":"Mehta","suffix":""}],"badges":[],"createdAt":"2025-04-15 08:54:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6452922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6452922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97370212,"identity":"c41e4294-ab11-44b9-82b2-5d2004af3194","added_by":"auto","created_at":"2025-12-03 16:26:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":457339,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6452922/v1_covered_f1305421-6dd4-4d70-b8ad-992eaac36919.pdf"}],"financialInterests":"","formattedTitle":"Analysis of Water Quality Using Machine Learning Techniques","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":"Water Quality Analysis, XGB, Random forest, Machine learning, Deep learning, Feature Extraction","lastPublishedDoi":"10.21203/rs.3.rs-6452922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6452922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe research aims to analyse water quality using Python to determine its potability. In this context, machine learning models: XGBoost and Random Forest, achieving accuracies of up to 80% and 78%, respectively. The process begins by feeding the mod- els a dataset containing various water quality metrics from multiple sources. These models are then trained to predict water potability, containing parameters like phys- ical, chemical and biological. which is crucial for ensuring safe drinking water. The XGBoost model, which is most noted for its great efficiency and efficacy in classifying issues, can handle large datasets that include a lot of varying variables and is there- fore suitable for complicated water quality datasets. 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