Research on Leak Localization Method for Water Supply Pipelines Based on Optimized Successive Variational Mode Decomposition and Generalized Cross-Correlation

Research on Leak Localization Method for Water Supply Pipelines Based on Optimized Successive Variational Mode Decomposition and Generalized Cross-Correlation | 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 Research on Leak Localization Method for Water Supply Pipelines Based on Optimized Successive Variational Mode Decomposition and Generalized Cross-Correlation Zhendong Zhao, Zhu Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8103371/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 To address the issue of significant noise interference and low time delay estimation accuracy in water pipeline leak localization, this paper proposes a leak localization method based on optimized Successive Variational Mode Decomposition (SVMD) and generalized cross-correlation (GCC). By introducing the Hippopotamus Optimization algorithm (HO) to adaptively optimize the penalty factor and Pearson correlation coefficient (PCC) threshold of SVMD, this method resolves the issue of traditional SVMD parameter selection relying on experience, significantly improving signal decomposition and denoising performance. Further, the GCC method is combined with time delay estimation for the denoised negative pressure wave (NPW) signals, resulting in the proposed SVMD-GCC method. Simulation experiments show that this method can still accurately estimate time delays in low signal-to-noise ratio (SNR) environments, and its noise suppression performance is superior to that of traditional GCC and VMD-GCC methods. Real pipeline leak experiments in the laboratory validated its localization accuracy and robustness, particularly maintaining high reliability under complex pipeline conditions. The method proposed in this paper provides a high-precision, adaptive solution for urban water supply pipeline leak detection, with significant engineering application value. Successive Variational Mode Decomposition (SVMD) Hippopotamus Optimization (HO) Generalized Cross-Correlation (GCC) Leak location Negative Pressure Wave (NPW) Delay estimation Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 11 Mar, 2026 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 13 Nov, 2025 First submitted to journal 13 Nov, 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-8103371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548193805,"identity":"15e58ded-532b-4108-a31d-a08e12a8f83e","order_by":0,"name":"Zhendong Zhao","email":"","orcid":"","institution":"Xihua University School of Energy and Power Engineering","correspondingAuthor":false,"prefix":"","firstName":"Zhendong","middleName":"","lastName":"Zhao","suffix":""},{"id":548193806,"identity":"2f176421-103d-4bff-ad6b-d1ac55116861","order_by":1,"name":"Zhu 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