An improved CNN model in image classification application on water turbidity

An improved CNN model in image classification application on water turbidity | 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 Article An improved CNN model in image classification application on water turbidity Ying Nie, Yuqiang Chen, Jianlan Guo, Shufei Li, Yu Xiao, Wendong Gong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4943120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Water turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNNs) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. This study has opened up new applications of CNN in fine-grained image classification, and further demonstrated the effectiveness of convolutional neural networks in water turbidity image classification through experiments. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Physical sciences/Mathematics and computing Water turbidity AI models CNN Accuracy Full Text Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.pptx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 22 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers invited by journal 09 Sep, 2024 Editor assigned by journal 09 Sep, 2024 Editor invited by journal 30 Aug, 2024 Submission checks completed at journal 29 Aug, 2024 First submitted to journal 20 Aug, 2024 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-4943120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":357587790,"identity":"61c3d04d-1cd1-44c6-9592-8b4f186991fa","order_by":0,"name":"Ying Nie","email":"","orcid":"","institution":"GuangDong Country Garden Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Nie","suffix":""},{"id":357587791,"identity":"57284e92-b5a4-45c0-a926-190b214e08ca","order_by":1,"name":"Yuqiang 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CNN, Accuracy","lastPublishedDoi":"10.21203/rs.3.rs-4943120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4943120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNNs) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. 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