HydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision

HydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision | 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 HydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision Shubham Laxmikant Deshmukh, Matthew Wilchek, Feras A. Batarseh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8532268/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 Ongoing advancements in Computer Vision, particularly in pattern recognition and scene classification, have paved the way for innovative environmental monitoring applications. Deep Learning has demonstrated promising results, enabling non-contact water quality monitoring and contamination assessment, both essential for disaster response and public health protection. This manuscript proposes HydroVision, a novel deep learning-based scene classification framework that estimates optically active water quality parameters, such as Chlorophyll-α, Chlorophylls, Colored Dissolved Organic Matter (CDOM), Phycocyanins, Suspended Sediments, and Turbidity, from Red-Green-Blue (RGB) images of surface water. It is trained on a diverse and extensive dataset of over 500,000 seasonally varied images sourced from the United States Geological Survey (USGS) Hydrologic Imagery Visualization and Information System (HIVIS) database between early 2022 and late 2024. The proposed model introduces an innovative approach to water quality monitoring and assessment using widely available RGB imagery, serving as a scalable and cost-effective alternative to traditional multispectral/hyperspectral remote sensing. The model is trained using four state-of-the-art Convolutional Neural Network (CNN) architectures: VGG-16, ResNet50, MobileNetV2, DenseNet121 and a Vision Transformer (ViT), leveraging transfer learning to determine the optimal framework for predicting six optically active parameters. Among them, the best-performing model achieves a validation R2 (coefficient of determination) score of 0.89 using DenseNet121 for predicting CDOM, underscoring its potential for real-world water quality assessment across diverse environmental conditions. Although trained on well-lit images, enhancing robustness under low-light or obstructed conditions offers a promising direction for expanding its practical utility. Water Quality Computer Vision Deep Learning Surface Water Optical Parameters Remote Sensing Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 10 Jan, 2026 Editor assigned by journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 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. 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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-8532268","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578153370,"identity":"45712b58-45c7-4cc1-af4d-1037d72b5b4c","order_by":0,"name":"Shubham Laxmikant Deshmukh","email":"","orcid":"","institution":"Virginia Tech: Virginia Polytechnic Institute and State University","correspondingAuthor":false,"prefix":"","firstName":"Shubham","middleName":"Laxmikant","lastName":"Deshmukh","suffix":""},{"id":578153371,"identity":"3e3effd1-ea9f-4d74-b2bf-ae8375e2503d","order_by":1,"name":"Matthew Wilchek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYFACxsYDjA0MBvxgDhtxWhrAWiQbmInWwsAA1mJwgFgtujOSGw783GFjbHwj/wDDh7LDhLWY3UhsONh7Js3M7EYyA+OMc8RouZ3YcJix7bCN2ZnDDMy8bcRr+W9j3APU8pcELQfMDNibGZgZidJy/yHQL23JxhLHmw0O9pxLJ0LLmeMPH/xsszPsb2Z8+OBHmTVhLSjgAInqR8EoGAWjYBTgAgDpvkGvkh1zyAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8664-1586","institution":"Virginia Tech: Virginia Polytechnic Institute and State University","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Wilchek","suffix":""},{"id":578153372,"identity":"e7ee5a51-2ce1-4086-8ec0-76a11556d962","order_by":2,"name":"Feras A. 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