Aging Water Distribution Networks: A Hybrid Spatial Decision Support and Machine Learning Framework for Leak Detection

Aging Water Distribution Networks: A Hybrid Spatial Decision Support and Machine Learning Framework for Leak Detection | 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 Aging Water Distribution Networks: A Hybrid Spatial Decision Support and Machine Learning Framework for Leak Detection Amir Noori, Ehsan Roshani, Hossein Bonakdari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9337863/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Leak detection in aging water distribution networks (WDNs) is a complex engineering challenge influenced by nonlinear hydraulic performance, infrastructure deterioration, spatial heterogeneity, and limited confirmed failure data. Purely sensor-based and data-driven approaches often face scalability constraints and rely on simplified assumptions, limiting robustness under real operating conditions. This study proposes a hybrid spatial predictive framework that integrates pressure-driven hydraulic simulation, GIS-based Fuzzy Analytic Hierarchy Process (FAHP), and supervised machine learning to identify leakage-prone nodes in large-scale WDNs. A pressure-driven EPANET model incorporating emitter coefficients and pipe aging effects simulates realistic leakage under diurnal demand patterns. A four-rule screening process identifies 4,699 high-risk nodes from over 39,000 nodes, followed by sensitivity–correlation analysis to determine influential nodes for efficient sensor placement. Spatial and infrastructural characteristics are quantified using Fuzzy AHP through a weighted evaluation of six criteria, with pipe age identified as the dominant factor (0.382). In addition, GIS-based fuzzy AHP enables the development of a spatial leakage risk map, indicating that more than 30% of the network falls within high (20.98%) and very high (11.06%) risk categories, reflecting significant vulnerability concentrated in aging infrastructure. These features are used to train multiple classifiers, among which Extreme Gradient Boosting (XGBoost) achieves the best performance (accuracy = 0.961; ROC–AUC = 0.989). Application to a full-scale urban WDN in southern Ontario demonstrates that the framework improves leak detection reliability and supports scalable, data-driven infrastructure management. Leak Detection Water Distribution Network Hydraulic Simulation Leakage Risk Machine Learning Fuzzy AHP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 May, 2026 Reviews received at journal 16 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 06 Apr, 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. 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-9337863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625362263,"identity":"d3d1fcc7-9ec1-4dd2-aa83-5db1da774477","order_by":0,"name":"Amir Noori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3NvQrCMBiF4UigXQpdI4K9hQShm3grlkCnDo4VBD8R4qZrwZsQvIGUgi7tHqiDIjh3cvSnOCqh3RzyzufhIGQy/W30id2WJMZ2F1AHZHOSY5fKpsTdLtPrVFi9geLHazXLPGRnZy0hpwNnhXAGvgoDkIeMgRNS/Y2K/O5CEO6riIG0yg4QpCfeh9D5PqnJoxwBsSstoTWBfIwpeZNUlAEQR//CVMgZxBKT/BYkxfrJhRNNtKSveHoBKrG74rKK7+FwYx93WvKd1XJvMplMph+9ACfvS3XaKdnbAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ottawa","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"","lastName":"Noori","suffix":""},{"id":625362264,"identity":"ac84ca5e-5d98-4278-a417-55fbbb8cd53e","order_by":1,"name":"Ehsan Roshani","email":"","orcid":"","institution":"National Research Council Canada","correspondingAuthor":false,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Roshani","suffix":""},{"id":625362265,"identity":"6e8ab0a9-188d-431f-93b9-a58a4aa6a484","order_by":2,"name":"Hossein Bonakdari","email":"","orcid":"","institution":"University of Ottawa","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Bonakdari","suffix":""}],"badges":[],"createdAt":"2026-04-07 00:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9337863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9337863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707822,"identity":"a37e721c-f611-4068-be6b-60559cb20b6d","added_by":"auto","created_at":"2026-04-24 09:21:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1590882,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptNoori.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9337863/v1_covered_e4485288-428a-4cf1-ab80-153620101c8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aging Water Distribution Networks: A Hybrid Spatial Decision Support and Machine Learning Framework for Leak Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environment-systems-and-decisions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"envr","sideBox":"Learn more about [Environment Systems and Decisions](http://link.springer.com/journal/10669)","snPcode":"10669","submissionUrl":"https://submission.nature.com/new-submission/10669/3","title":"Environment Systems and Decisions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Leak Detection, Water Distribution Network, Hydraulic Simulation, Leakage Risk, Machine Learning, Fuzzy AHP","lastPublishedDoi":"10.21203/rs.3.rs-9337863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9337863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLeak detection in aging water distribution networks (WDNs) is a complex engineering challenge influenced by nonlinear hydraulic performance, infrastructure deterioration, spatial heterogeneity, and limited confirmed failure data. Purely sensor-based and data-driven approaches often face scalability constraints and rely on simplified assumptions, limiting robustness under real operating conditions. This study proposes a hybrid spatial predictive framework that integrates pressure-driven hydraulic simulation, GIS-based Fuzzy Analytic Hierarchy Process (FAHP), and supervised machine learning to identify leakage-prone nodes in large-scale WDNs. A pressure-driven EPANET model incorporating emitter coefficients and pipe aging effects simulates realistic leakage under diurnal demand patterns. A four-rule screening process identifies 4,699 high-risk nodes from over 39,000 nodes, followed by sensitivity\u0026ndash;correlation analysis to determine influential nodes for efficient sensor placement. Spatial and infrastructural characteristics are quantified using Fuzzy AHP through a weighted evaluation of six criteria, with pipe age identified as the dominant factor (0.382). 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