The effect of data variations on the leakage detection model performance using a convolutional neural network (CNN)

The effect of data variations on the leakage detection model performance using a convolutional neural network (CNN) | 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 The effect of data variations on the leakage detection model performance using a convolutional neural network (CNN) Muhammad Anshari Caronge, Yasuhiro Arai, Kaito Ito, Takaharu Kunizane, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3811073/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In recent years, leakage detection models using artificial intelligence have been widely used by researchers. In this study, acoustic sound data were recorded at 10 observation points, focusing on ductile iron pipe and vinyl polyethylene. Leakage detection models were built using recurrence plots (RPs) and a convolutional neural network (CNN). Using the same number of RPs for training data and testing data, we analyzed the effect of data variations on model performance. The results showed that our proposed approach can improve accuracy at several points, although the amount of training data information was advantageous in previous work. There were cases in previous work that had poor accuracy, but when implementing our proposed approach, it improved the accuracy to over 80% when using the 8-point model. The increase in accuracy depends on which interval is used in the test data because each interval contains information about different properties. For multi-point models, the effect of increasing the number of RPs and data variations was not clarified in the previous work. However, our study confirmed that the increase in data variation contributed more than the RP number. convolutional neural network data variation model performance multi-point model recurrence plots Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Jan, 2024 Reviewers invited by journal 13 Jan, 2024 Editor assigned by journal 27 Dec, 2023 First submitted to journal 26 Dec, 2023 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-3811073","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266966687,"identity":"afb24f3f-9dc5-4e11-86e8-4c160dbbec9b","order_by":0,"name":"Muhammad Anshari 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