A Climate-Informed Early Warning Framework for Urban Water Pipe Leakage: Integrating Environmental Drivers with LSTM Based Risk Prediction

A Climate-Informed Early Warning Framework for Urban Water Pipe Leakage: Integrating Environmental Drivers with LSTM Based Risk Prediction | 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 A Climate-Informed Early Warning Framework for Urban Water Pipe Leakage: Integrating Environmental Drivers with LSTM Based Risk Prediction Hessam Najafi, Zixue He, Jinhui Jeanne Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7089419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Frequent leakage events in aging urban water distribution networks (WDNs) pose increasing challenges under evolving climate and infrastructure stressors. This study develops a climate-informed early warning framework for leakage risk assessment, applied to K-City, a subtropical highland city in China. To support model design, meteorological variables (temperature metrics, precipitation), soil moisture (SM) and their seasonal impacts on leakage occurrence were first analyzed. Consumption-related dynamics and pressure-flow interactions were further examined to understand their role in system stress. The analysis revealed heightened vulnerability in smaller-diameter pipelines and during dry, post-monsoon periods. Building on these insights, a Long Short-Term Memory (LSTM) model was developed to perform both point prediction of daily leakage numbers (DLNs) and classification of leakage risk into four severity levels. Model performance was benchmarked against Random Forest (RF) and Extreme Gradient Boosting (XGBoost) alternatives, and interpretability was enhanced using Shapley Additive Explanations (SHAP) to assess the contribution of each input variable. A 7-day input lag yielded the best results, enabling early warning of leakage risks up to one week in advance. While point prediction accuracy was moderate, the LSTM model demonstrated robust classification performance, achieving a total accuracy (TA) of 0.78 and an F1-score of 0.70 across all pipe categories and outperforming both RF and XGBoost despite the complexity of multi-class leakage prediction. The proposed framework demonstrates high potential for practical deployment in proactive leakage management, particularly in resource-constrained urban settings. Water distribution networks Leakage risk prediction Environmental drivers Early warning system Long Short-Term Memory Full Text Supplementary Files AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Jan, 2026 Editor invited by journal 23 Jan, 2026 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 10 Jul, 2025 First submitted to journal 10 Jul, 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. 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-7089419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492937086,"identity":"fe491e75-7017-4a6d-a6af-f001f0071ba6","order_by":0,"name":"Hessam Najafi","email":"","orcid":"","institution":"Nankai University College of Environmental Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Hessam","middleName":"","lastName":"Najafi","suffix":""},{"id":492937087,"identity":"ec592bd7-3cb1-46f9-a386-00f5fd1bbf29","order_by":1,"name":"Zixue He","email":"","orcid":"","institution":"Nankai University College of Environmental Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Zixue","middleName":"","lastName":"He","suffix":""},{"id":492937088,"identity":"4fcdc04d-c392-4e40-bcca-9fb5457487a9","order_by":2,"name":"Jinhui Jeanne Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACAzDJYwMk2BgYEhgYGBuI1JJGshaGwxAtDMRoMZdIfvbwi8z5PP7ZbYkfHjDYyG44wPzsAT4tljPSzI1leG4XS9w5dlgigSHNeMMBNnMDvA67kWAmLcFzO7HhRnoDUMvhxA0HeNgk8GtJ/wbUci5x/o305h8JDP+J0ZJjJvmB50Dihhtpx4C2HCBCy5k3ZdIMPMmJG2+kpVkkGCQbzzzMZoZfy/H0bZI/e+wS591IM775o8JOtu948zO8WkCAmbcHbgKIS0g9EDD++EGEqlEwCkbBKBi5AAC1VEy/srz+OgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5268-1747","institution":"Nankai University College of Environmental Science and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Jinhui","middleName":"Jeanne","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-07-10 06:09:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7089419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7089419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88281657,"identity":"c2286de6-a0f5-4556-9183-3c657b5b4671","added_by":"auto","created_at":"2025-08-04 20:23:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312468,"visible":true,"origin":"","legend":"","description":"","filename":"AClimateInformedEarlyWarningFramework.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7089419/v1_covered_08a4a60d-00ae-4b84-8f85-53c82351effe.pdf"},{"id":88280329,"identity":"72552686-6ae5-428f-a007-698ae42e5e32","added_by":"auto","created_at":"2025-08-04 19:51:47","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":616540,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7089419/v1/d9565aa68ea3d13117ee51a9.docx"}],"financialInterests":"","formattedTitle":"A Climate-Informed Early Warning Framework for Urban Water Pipe Leakage: Integrating Environmental Drivers with LSTM Based Risk Prediction","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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water distribution networks, Leakage risk prediction, Environmental drivers, Early warning system, Long Short-Term Memory","lastPublishedDoi":"10.21203/rs.3.rs-7089419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7089419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFrequent leakage events in aging urban water distribution networks (WDNs) pose increasing challenges under evolving climate and infrastructure stressors. 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