IoT-Enabled Water Quality Assessment and Demand Prediction for Sustainable Water Management

IoT-Enabled Water Quality Assessment and Demand Prediction for Sustainable Water Management | 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 IoT-Enabled Water Quality Assessment and Demand Prediction for Sustainable Water Management Vasifa Sameer kotwal, Sangram Patil, Jaydeep Patil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6470418/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Water resource management is a critical challenge in urban planning, requiring accurate demand forecasting to ensure sustainable distribution. This study implements Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict water consumption patterns using historical data and key influencing factors such as population growth, temperature, and rainfall. Three models—Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) Neural Network—were evaluated based on prediction accuracy, error metrics, and computational efficiency. The results indicate that LSTM achieved the highest accuracy, with prediction scores of 0.88 for daily, 0.87 for monthly, and 0.86 for yearly forecasts. Additionally, the model outperformed others in error reduction, achieving a Mean Absolute Error (MAE) of 1.5. The study demonstrates the potential of AI-driven forecasting in optimizing water distribution and conservation strategies. Future research may incorporate real-time IoT sensor data and deep learning models to enhance predictive accuracy and adaptive resource allocation Water Management Machine Learning Demand Prediction IoT ARIMA Model Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 05 Mar, 2026 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 21 Apr, 2025 First submitted to journal 21 Apr, 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. 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-6470418","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453844608,"identity":"e585e068-d312-404f-a03a-daf8b17727e7","order_by":0,"name":"Vasifa Sameer kotwal","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-9876-0226","institution":"D Y Patil University","correspondingAuthor":true,"prefix":"","firstName":"Vasifa","middleName":"Sameer","lastName":"kotwal","suffix":""},{"id":453844609,"identity":"ee82b637-7f07-4f66-8555-39c8bcf1d9f6","order_by":1,"name":"Sangram Patil","email":"","orcid":"","institution":"D Y Patil University","correspondingAuthor":false,"prefix":"","firstName":"Sangram","middleName":"","lastName":"Patil","suffix":""},{"id":453844610,"identity":"567aa41f-8d55-4bde-8931-fe8cbd309014","order_by":2,"name":"Jaydeep Patil","email":"","orcid":"","institution":"D Y Patil University","correspondingAuthor":false,"prefix":"","firstName":"Jaydeep","middleName":"","lastName":"Patil","suffix":""}],"badges":[],"createdAt":"2025-04-17 09:43:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6470418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6470418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82571708,"identity":"64f2be3b-80b8-479d-96c5-be15f35e6d6c","added_by":"auto","created_at":"2025-05-13 04:27:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425741,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureVasifaKotwal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6470418/v1_covered_88d52dc5-1ebc-4edb-b2b6-6432d8f15d43.pdf"}],"financialInterests":"","formattedTitle":"IoT-Enabled Water Quality Assessment and Demand Prediction for Sustainable Water Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water Management, Machine Learning, Demand Prediction, IoT, ARIMA Model","lastPublishedDoi":"10.21203/rs.3.rs-6470418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6470418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Water resource management is a critical challenge in urban planning, requiring accurate demand forecasting to ensure sustainable distribution. 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