Developing a new Artificial Intelligence framework to estimate the thalweg of rivers

Developing a new Artificial Intelligence framework to estimate the thalweg of rivers | 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 Developing a new Artificial Intelligence framework to estimate the thalweg of rivers Zohre Aghamolaei, Masoud Reza Hessami Kermani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3145167/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Oct, 2023 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract Hydrographic operations to investigate the riverbed form throughout the entire length of a river are costly and time-consuming. This has made scholars use a wide range of alternative methods to address the issue. In the present study, however, a new framework using Artificial Intelligence- (AI-) based models is introduced to identify the thalweg of rivers, which provides an accurate estimate of a river thalweg via linking coordinates of their left and right banks. In this regard, we trained and tested the performance of two AI-based models, including Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. The database of two rivers, namely the Qinhe River in China and the Gaz River in Iran was used to help evaluate the developed model. Outcomes of the two investigated case studies demonstrated that the values of the statistical error estimators, including the Root Mean Square Error (RMSE) of the ANFIS model were less than those of the ANN model. As a result, the ANFIS model can lead to more accurate results than the ANN model, and it is suitable for cases with less available data. Moreover, comparing the results from the developed models with those of the River Channel Morphology Model (RCMM) showed that AI-based models outdo numerical approaches in the identification of the thalweg of rivers. All in all, it is inferred that the proposed approach not only helps us achieve an accurate geometry of rivers but reduces the side costs and can be used as an effective alternative to field operations. Thalweg Hydraulic geometry relationships Artificial Intelligence ANFIS ANN Gaz River Full Text Cite Share Download PDF Status: Published Journal Publication published 25 Oct, 2023 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Major revisions 05 Aug, 2023 Reviewers agreed at journal 25 Jul, 2023 Reviewers invited by journal 25 Jul, 2023 Editor assigned by journal 09 Jul, 2023 First submitted to journal 06 Jul, 2023 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-3145167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":221426122,"identity":"3eb869a6-14c8-444d-9367-8670bbc0f8bb","order_by":0,"name":"Zohre Aghamolaei","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zohre","middleName":"","lastName":"Aghamolaei","suffix":""},{"id":221426123,"identity":"6c5af3f8-f490-4bc4-a380-1531570799f5","order_by":1,"name":"Masoud Reza Hessami Kermani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACAwglIcfAA2EQr8WYZC0MiQ08xDrMnL398YefORbpG84cYPzwg8Ein6AWy54zZpK92yRyN5xtYJbsYZCwbCDosBs5bAy8IC3nGRikgX4xIKSDweD+88cf/26TSDc4z8D8mzgtNxgMpIG2JBicbWAj0pYzOWbSstskDGeeOdhm2WNAjJbjxx9/fLutTp7vTPLhGz8q6ghrQQKMDYhoGgWjYBSMglFAGQAAvzM2XfsUbWQAAAAASUVORK5CYII=","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"Reza Hessami","lastName":"Kermani","suffix":""}],"badges":[],"createdAt":"2023-07-06 09:10:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3145167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3145167/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11269-023-03632-8","type":"published","date":"2023-10-25T15:02:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":45453971,"identity":"bd0b042f-30bf-4943-a7e0-47bc63e6a7dc","added_by":"auto","created_at":"2023-10-30 15:08:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960727,"visible":true,"origin":"","legend":"","description":"","filename":"AghamolaeiAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3145167/v1_covered_dfa0b9c4-0b41-4ed6-9662-9da38c14794e.pdf"}],"financialInterests":"","formattedTitle":"Developing a new Artificial Intelligence framework to estimate the thalweg of rivers","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":"Thalweg, Hydraulic geometry relationships, Artificial Intelligence, ANFIS, ANN, Gaz River","lastPublishedDoi":"10.21203/rs.3.rs-3145167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3145167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrographic operations to investigate the riverbed form throughout the entire length of a river are costly and time-consuming. 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As a result, the ANFIS model can lead to more accurate results than the ANN model, and it is suitable for cases with less available data. Moreover, comparing the results from the developed models with those of the River Channel Morphology Model (RCMM) showed that AI-based models outdo numerical approaches in the identification of the thalweg of rivers. 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