Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs

Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs | 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 Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs Yeganeh Seif, Ali Arman, Mostafa Rahmanshahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4110223/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Environmentally friendly porous weirs have attracted the attention of researchers and engineers due to their favorable characteristics, surpassing solid weirs in terms of environmental impact, hydraulic performance, and stability. However, accurately estimating the submerged discharge coefficient for porous weirs is challenging due to the complex flow mechanisms involved, particularly under submerged conditions. The discharge under submerged conditions is typically expressed as a multiple of the free flow discharge, along with a coefficient representing the submerged discharge reduction factor (SDRF). This study aims to propose a novel artificial intelligence framework that incorporates metaheuristic techniques to predict SDRF for porous broad-crested weirs (PBCWs). The research utilized generalized normal distribution optimization (GNDO) to optimize the multilayer perceptron (MLP) model, enabling more precise predictions. The performance of the hybrid MLP-GNDO model was compared to that of an MLP, gene-expression programming (GEP), and standard nonlinear regression (SNR) models. A dataset comprising 966 observed experiments was employed to evaluate the proposed models. The results demonstrated that the hybrid MLP-GNDO model outperformed the MLP, GEP, and SR models, achieving a root mean square error of 0.021 and 0.022 and an R 2 value of 0.964 and 0.954 for the training and test datasets, respectively. This model accurately predicted the train and test datasets with an average error rate of less than 2%. Regarding accuracy, the models ranked in the following order: MLP, GEP, and SNR. discharge coefficient porous weir multilayer perceptron generalized normal distribution optimization gene-expression programming standard nonlinear regression Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 09 Jul, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers invited by journal 26 Jun, 2024 First submitted to journal 16 Mar, 2024 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-4110223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319227425,"identity":"bc5a6a1e-1c25-47c3-9d8b-946c659a4f3c","order_by":0,"name":"Yeganeh Seif","email":"","orcid":"","institution":"Razi University of Kermanshah: Razi University","correspondingAuthor":false,"prefix":"","firstName":"Yeganeh","middleName":"","lastName":"Seif","suffix":""},{"id":319227426,"identity":"6af81e2c-6043-44c8-8209-1d59a62ee9ca","order_by":1,"name":"Ali Arman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIie3OsQqDMBCA4SuBdAm6Bgr6CpXsPkvEwUWnrg5OuhRnoX2LDl0PArr0ATp0kYKztOAorVunJt065CMc3PBzAbCsP+YtA5dBTRPxexIVxh9yCzI8rvktOVddj5CH4Gzwe8KRikPaDtnxIiVCGwN1pOYMgiApVVkDEhHo+zFN4eP6SdJZJdztC4TZINkiEyQrleQ8BlyVBkmg2I5ktQoaPgBGdcy0iddVJ5JOyuduch/HKfT8vSYB8rlIAN0Ny7Isy8QL8go7cZgMazYAAAAASUVORK5CYII=","orcid":"","institution":"Razi University of Kermanshah: Razi University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Arman","suffix":""},{"id":319227427,"identity":"03b3ee49-541f-4820-9f50-edb07c57c440","order_by":2,"name":"Mostafa Rahmanshahi","email":"","orcid":"","institution":"Hong Kong Polytechnic University Department of Mechanical Engineering","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Rahmanshahi","suffix":""}],"badges":[],"createdAt":"2024-03-15 20:43:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4110223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4110223/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60553583,"identity":"f25c43e9-90c1-4089-a8f2-47274a6fdba6","added_by":"auto","created_at":"2024-07-18 05:54:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170031,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4110223/v1_covered_17a4d1a0-dd7d-431d-9dd3-b87c583055d4.pdf"}],"financialInterests":"","formattedTitle":"Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"discharge coefficient, porous weir, multilayer perceptron, generalized normal distribution optimization, gene-expression programming, standard nonlinear regression","lastPublishedDoi":"10.21203/rs.3.rs-4110223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4110223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmentally friendly porous weirs have attracted the attention of researchers and engineers due to their favorable characteristics, surpassing solid weirs in terms of environmental impact, hydraulic performance, and stability. However, accurately estimating the submerged discharge coefficient for porous weirs is challenging due to the complex flow mechanisms involved, particularly under submerged conditions. The discharge under submerged conditions is typically expressed as a multiple of the free flow discharge, along with a coefficient representing the submerged discharge reduction factor (SDRF). This study aims to propose a novel artificial intelligence framework that incorporates metaheuristic techniques to predict SDRF for porous broad-crested weirs (PBCWs). The research utilized generalized normal distribution optimization (GNDO) to optimize the multilayer perceptron (MLP) model, enabling more precise predictions. The performance of the hybrid MLP-GNDO model was compared to that of an MLP, gene-expression programming (GEP), and standard nonlinear regression (SNR) models. A dataset comprising 966 observed experiments was employed to evaluate the proposed models. The results demonstrated that the hybrid MLP-GNDO model outperformed the MLP, GEP, and SR models, achieving a root mean square error of 0.021 and 0.022 and an R\u003csup\u003e2\u003c/sup\u003e value of 0.964 and 0.954 for the training and test datasets, respectively. This model accurately predicted the train and test datasets with an average error rate of less than 2%. 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