A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

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dc.contributor.authorGharehbaghi, Amin
dc.contributor.authorGhasemlounia, Redvan
dc.contributor.authorAfaridegan, Ehsan
dc.contributor.authorHaghiabi, AmirHamzeh
dc.contributor.authorMandala, Vishwanadham
dc.contributor.authorAzamathulla, Hazi Mohammad
dc.contributor.authorParsaie, Abbas
dc.date.accessioned2024-06-13T20:18:27Z
dc.date.available2024-06-13T20:18:27Z
dc.date.issued2023
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractIn the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%.
dc.description.sponsorshipResearch Council of Shahid Chamran University of Ahvaz [SCU.WH1401.7209]
dc.identifier.doi10.2166/hydro.2023.063
dc.identifier.endpage1530
dc.identifier.issn1464-7141
dc.identifier.issn1465-1734
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85162927564
dc.identifier.scopusqualityQ2
dc.identifier.startpage1513
dc.identifier.urihttps://doi.org/10.2166/hydro.2023.063
dc.identifier.urihttps://hdl.handle.net/11501/1377
dc.identifier.volume25
dc.identifier.wosWOS:000999085500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhasemlounia, Redvan
dc.institutionauthorid0000-0003-1796-4562
dc.language.isoen
dc.publisherIWA Publishing
dc.relation.ispartofJournal of Hydroinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDischarge Coefficient
dc.subjectIntelligent Optimization Models
dc.subjectJoukowsky Transform
dc.subjectMachine-Learning Models
dc.subjectOpen-Channel Flow
dc.subjectStreamlined Weirs
dc.titleA comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs
dc.typeArticle

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