Continuous small leakage identification method of urban pipeline based on improved MVMD fusion machine learning

Continuous small leakage identification method of urban pipeline based on improved MVMD fusion machine learning | 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 Continuous small leakage identification method of urban pipeline based on improved MVMD fusion machine learning Anning Wang, Yongmei Hao, Zhixiang Xing, Zhicheng Wang, Jun Shen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6332416/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Oct, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 11 You are reading this latest preprint version Abstract To address the challenge that continuous small leakage signals are easily disrupted by noise, resulting in a low recognition rate for urban pipeline leakage, we propose an improved multivariate variational mode decomposition (IMVMD) fusion machine learning method specifically for the recognition of continuous small leakages in urban pipelines. Building upon the preliminary time-frequency assessment of the original leakage signal, we enhance the MVMD by incorporating the correlation coefficient and normalized Shannon entropy, enabling adaptive decomposition and reconstruction of the leakage signals. We establish a BP neural network based on the IMVMD and a SVM leakage recognition model also based on IMVMD. Random forest (RF) evaluation is employed to identify the signal feature inputs. The results indicate that the signal-to-noise ratio of the reconstructed signal using IMVMD is 55.42% higher than that of the original signal, demonstrating a superior decomposition effect compared to traditional MVMD EMD and VMD. RF is utilized to reduce the dimensionality of signal characteristics under various leakage conditions, resulting in the selection of four representative features: root mean square, short-term energy, margin factor, and waveform factor, which serve as inputs for the BP neural network and SVM leakage recognition model based on IMVMD. The accuracy of signal recognition reaches 98.22% and 97.22%, respectively. Compared to the traditional MVMD decomposition recognition model, this method improves accuracy by 10.72% and 10.22%, respectively, thereby providing reliable support for the detection and precise localization of continuous small leakages in urban pipelines. Leakage recognition Feature selection Infrasonic signals MVMD Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Oct, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 31 Jul, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviews received at journal 17 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 01 May, 2025 Submission checks completed at journal 29 Mar, 2025 First submitted to journal 29 Mar, 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. 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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-6332416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456444866,"identity":"188177f8-f25f-45d0-9f27-a294c7da22dd","order_by":0,"name":"Anning Wang","email":"","orcid":"","institution":"a：Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Anning","middleName":"","lastName":"Wang","suffix":""},{"id":456444868,"identity":"4fe63954-258c-4c8d-b494-cb4b73a2ffe8","order_by":1,"name":"Yongmei 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