Predicting Water Pipe Failures with Graph Neural Networks: Integrating Coupled Road and Pipeline Features

Predicting Water Pipe Failures with Graph Neural Networks: Integrating Coupled Road and Pipeline Features | 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 Predicting Water Pipe Failures with Graph Neural Networks: Integrating Coupled Road and Pipeline Features Qunfang Hu, Yu Zhang, Wen Liu, Lei He, Delu Che, Zhan Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4249898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The reliability of urban water distribution networks (WDNs) is critical for public health and safety. This study presents a novel approach to predicting WDN failures by leveraging Graph Neural Networks (GNNs) and incorporating coupled features of road and water networks, with an emphasis on traffic-related characteristics. Our framework employs Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE to capture the complex spatial dependencies and interactions between road infrastructure and water pipelines. We evaluate the performance of these models using a dataset from a Chinese city, focusing on metrics such as Area Under the Curve (AUC), accuracy, and recall. Our results indicate that GraphSAGE outperforms other models, demonstrating its effectiveness in leveraging neighborhood information for failure prediction. The analysis of feature importance highlights the significance of traffic-related attributes, such as the distance of pipelines from the center of intersections, road grades, and the angle of pipelines relative to roads, in addition to traditional factors like pipeline length, diameter, and age. By integrating these coupled features, our study offers a more accurate and comprehensive understanding of failure risks, providing valuable insights for proactive maintenance and management of urban WDNs. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Scientific data Water Distribution Networks (WDNs) Graph Neural Networks (GNNs) Urban Infrastructure Coupled Networks Pipeline Failure Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4249898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294631306,"identity":"25ef83aa-67c1-4da3-92a8-7348e832c857","order_by":0,"name":"Qunfang Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACAzBpYwPhJDAwMDYQpyUtTYJkLYclYAKEtZhLJG+TLkg4X8cv3X6h4AGDjeyGA8zPHuDTYjkjrUx6RsJtCck5ZwqADksz3nCAzdwAr8Nu5JhJ8/64LQFkJAC1HE7ccICHTYKgFp6EczAt/4nWcgCoJf0AUMsBIrSceVZszZOQLDlzRg4wkA2SjWceZjPDr+V48sbbPAl2/PwS6c8Mf1TYyfYdb36GVwsDLGoYGHjMDMBsZgLqkbSwP35AWPEoGAWjYBSMRAAAYNVHJL3xYXwAAAAASUVORK5CYII=","orcid":"","institution":"Urban Mobility Institute, Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Qunfang","middleName":"","lastName":"Hu","suffix":""},{"id":294631307,"identity":"71e88ad9-8b6c-4f90-972c-29e23c40f4a2","order_by":1,"name":"Yu Zhang","email":"","orcid":"","institution":"Urban Mobility Institute, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":294631308,"identity":"67473b9c-503a-4c9c-8c14-11e9045ac314","order_by":2,"name":"Wen Liu","email":"","orcid":"","institution":"Development Center of Shanghai Road and Transportation","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Liu","suffix":""},{"id":294631309,"identity":"a640ff3f-c0ef-4273-a283-7abe93ff3fe1","order_by":3,"name":"Lei He","email":"","orcid":"","institution":"Shanghai Institute of Disaster Prevention and Relief, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"He","suffix":""},{"id":294631310,"identity":"c03313b8-49e7-4bfd-aaff-b148c50379d6","order_by":4,"name":"Delu Che","email":"","orcid":"","institution":"College of Civil Engineering, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Delu","middleName":"","lastName":"Che","suffix":""},{"id":294631311,"identity":"b92e3691-b738-4008-8377-2cb5f4543737","order_by":5,"name":"Zhan Su","email":"","orcid":"","institution":"Shanghai Institute of Disaster Prevention and Relief, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2024-04-11 02:46:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4249898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4249898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76257981,"identity":"0b976757-521c-4224-8e0a-6a6f64a1cc0c","added_by":"auto","created_at":"2025-02-14 05:33:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4249898/v1_covered_41b281b1-8081-4d15-ac24-da9ee64fc40c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Water Pipe Failures with Graph Neural Networks: Integrating Coupled Road and Pipeline Features","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water Distribution Networks (WDNs), Graph Neural Networks (GNNs), Urban Infrastructure, Coupled Networks, Pipeline Failure Prediction","lastPublishedDoi":"10.21203/rs.3.rs-4249898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4249898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe reliability of urban water distribution networks (WDNs) is critical for public health and safety. This study presents a novel approach to predicting WDN failures by leveraging Graph Neural Networks (GNNs) and incorporating coupled features of road and water networks, with an emphasis on traffic-related characteristics. Our framework employs Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE to capture the complex spatial dependencies and interactions between road infrastructure and water pipelines. We evaluate the performance of these models using a dataset from a Chinese city, focusing on metrics such as Area Under the Curve (AUC), accuracy, and recall. Our results indicate that GraphSAGE outperforms other models, demonstrating its effectiveness in leveraging neighborhood information for failure prediction. 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