A novel multivariate approach for water quality index prediction irrespective of the geospatial distinction of water sources having varied end uses

A novel multivariate approach for water quality index prediction irrespective of the geospatial distinction of water sources having varied end uses | 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 A novel multivariate approach for water quality index prediction irrespective of the geospatial distinction of water sources having varied end uses Himanchal Bhardwaj, Rajkumar Satankar, Deeptha Giridharan, Deepika Bhattu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8392489/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 quality assessment is fundamental to understanding the suitability of water for human consumption, ecological sustainability, and industrial applications. This study presents an empirical multi-parameter approach for estimating the Canadian Council of Ministers of the Environment (CCME)-based Water Quality Index (WQI) in Kota, Rajasthan, India. Eighteen samples from groundwater, surface water, and municipal supplies were analyzed for six parameters—pH, turbidity, total dissolved solids (TDS), hardness, alkalinity, and iron—following BIS IS 10500:2012 standards. Pareto analysis revealed turbidity and TDS as the most influential, independent drivers of WQI. GIS-based mapping captured spatial and seasonal variation, highlighting persistent water quality stress across the city. A quotient response function was developed using normalized parameters, and a predictive regression model was formulated with TDS and turbidity as core variables. The model achieved high accuracy, with R² values of 91.45% for groundwater, 96.05% for surface water, and 94.37% for municipal water. The results establish a scalable, source-independent framework for predicting WQI, offering robust support for urban water resource monitoring and sustainable management. CCME water quality regression GIS Rajasthan Full Text Supplementary Files EditorCommentsESPR.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 02 Jan, 2026 First submitted to journal 29 Dec, 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. 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-8392489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573357795,"identity":"403268cc-5a66-4b8b-9a25-42ac143fe466","order_by":0,"name":"Himanchal 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