A Data-Driven Approach to Dam Infrastructure Monitoring: Enhancing Prediction Accuracy by Systematic Rainfall Event Classification

A Data-Driven Approach to Dam Infrastructure Monitoring: Enhancing Prediction Accuracy by Systematic Rainfall Event Classification | 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 Data-Driven Approach to Dam Infrastructure Monitoring: Enhancing Prediction Accuracy by Systematic Rainfall Event Classification Ryul Kim, Soon Ho Kwon, Seung yub Lee, Young Hwan Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6818160/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract The safety of aging dam infrastructure has emerged as a critical issue as many social infrastructure systems experience functional degradation over time. While numerous monitoring devices have been deployed to evaluate dam conditions, the reliability of measurement data is often compromised due to harsh environmental factors, missing values, and abnormal fluctuations. These challenges highlight the necessity of ensuring data quality through both accurate point-level validation and comprehensive pattern analysis. Existing approaches often fail to systematically distinguish between environmental conditions such as rainfall and non-rainfall events, limiting the predictive reliability of artificial intelligence (AI) models in dam safety applications. To address this limitation, this study proposes a robust prediction framework that combines targeted data preprocessing with an eXtreme Gradient Boosting (XGBoost) model to enhance the reliability of dam measurement forecasts. Measurement data from infiltration turbidity and leakage meters were categorized based on rainfall occurrence to reflect environmental variability. The preprocessing method emphasizes structured training data reconstruction rather than the simple removal of outliers by identifying and separating time segments influenced by rainfall events. The proposed approach allows the model to better capture complex nonlinear patterns in dam behavior and improves prediction accuracy across various dam conditions. This study offers a practical contribution to the development of high-reliability forecasting systems for dam monitoring and lays the groundwork for future adaptive dam safety management strategies. Dam Safety monitoring Predictive maintenance Rainfall event classification Artificial Intelligence Model Structural anomaly detection Full Text Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Major revisions 16 Sep, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 22 Jul, 2025 Editor assigned by journal 04 Jun, 2025 First submitted to journal 04 Jun, 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-6818160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489218667,"identity":"50bca288-1c55-4059-8aec-9e2f6541e790","order_by":0,"name":"Ryul Kim","email":"","orcid":"","institution":"Gyeongsang National University","correspondingAuthor":false,"prefix":"","firstName":"Ryul","middleName":"","lastName":"Kim","suffix":""},{"id":489218668,"identity":"6833e73f-ef4e-42fa-8ea4-7f9946268f9c","order_by":1,"name":"Soon Ho Kwon","email":"","orcid":"","institution":"Hannam University","correspondingAuthor":false,"prefix":"","firstName":"Soon","middleName":"Ho","lastName":"Kwon","suffix":""},{"id":489218669,"identity":"2dbfdb2c-b982-4e81-80c7-245c587840ba","order_by":2,"name":"Seung yub Lee","email":"","orcid":"","institution":"Hannam University","correspondingAuthor":false,"prefix":"","firstName":"Seung","middleName":"yub","lastName":"Lee","suffix":""},{"id":489218670,"identity":"9d620c87-a79b-41de-9bb6-fa2c19ca51c5","order_by":3,"name":"Young Hwan Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCQYGZiApB+MbEK3FmGQtDIkNRGsxuN3A+LmgwiJ9fnSPAcOPGgZj8wZCWu4cYJaecUYid+OdMwaMPccYzGQOENJyI4GNmbcNqGVGjgEDbwODjQRBh0G1pBsCtTD+JUVLgrxEjgEz0BYzglokbyQ2S/OckTDcIJFWcFjmmIQxQS18N5IPfuapqJOXn5G88eGbGhvDGYS0KBxgbIC48AADwwFwNBEC8g3ojFEwCkbBKBgF6AAALNI2qryiPoUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1814-7989","institution":"Gyeongsang National University","correspondingAuthor":true,"prefix":"","firstName":"Young","middleName":"Hwan","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2025-06-04 08:35:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6818160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6818160/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11269-025-04373-6","type":"published","date":"2025-12-23T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172250,"identity":"60f21ad5-e115-4f3b-a70b-982f909c45a6","added_by":"auto","created_at":"2025-12-29 16:05:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1038723,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptfinalver.3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6818160/v1_covered_a905155f-9be7-42c5-9921-8a39798d441a.pdf"}],"financialInterests":"","formattedTitle":"A Data-Driven Approach to Dam Infrastructure Monitoring: Enhancing Prediction Accuracy by Systematic Rainfall Event Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Dam Safety monitoring, Predictive maintenance, Rainfall event classification, Artificial Intelligence Model, Structural anomaly detection","lastPublishedDoi":"10.21203/rs.3.rs-6818160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6818160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe safety of aging dam infrastructure has emerged as a critical issue as many social infrastructure systems experience functional degradation over time. 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