Application of AI approaches to estimate discharge coefficient of novel kind of sharp-crested v-notch weirs

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dc.contributor.authorGharehbaghi, Amin
dc.contributor.authorGhasemlounia, Redvan
dc.date.accessioned2024-06-13T20:18:07Z
dc.date.available2024-06-13T20:18:07Z
dc.date.issued2022
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractIn this study, the hydraulic features of the SCVW (a novel type of sharp-crested V-notch weirs) were scrutinized in the popular vertex angles theta, i.e., 30 degrees, 45 degrees, 60 degrees, 90 degrees, 120 degrees, 128 degrees, and 150 degrees, under aerated, steady and free overflow conditions in an open channel for large physical models. To assess the changes of the discharge coefficient of the SCVW (i.e., C-dSCVW) versus weir height and theta, widespread laboratory works were performed by measuring the water head over the crest of the weir and the discharge. Special formulas for the C-dSCVW in the theta=60 degrees were checked, and an appropriate empirical equation was recommended. The calculated C-dSCVW by the proposed equation was within 0%-10% of the measured values. Three types of nonparametric artificial intelligence (AI) methods, namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) were developed to estimate the C-dSCVW. For the sake of modeling, 196 experimental datasets were applied in the mentioned methods to evaluate the C-dSCVW by taking into consideration the dimensionless variables which impact the determining procedure of the C-dSCVW. In this modeling, by varying the architecture and core factors of the aforementioned methods, several scenarios were defined. The generated mathematical equation of C-dSCVW by the best scenario of the GEP was compared with the corresponding measured values in which the results were in 0%-10%. According to the attained statistical indices, scatter plots, and the values of the total grade (TG) technique, the hybrid SVR(RBF)-ACO model was determined as the superior and optimal method to estimate the C-dSCVW with high performance and accuracy.
dc.identifier.doi10.1061/(ASCE)IR.1943-4774.0001646
dc.identifier.issn0733-9437
dc.identifier.issn1943-4774
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85122822852
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1061/(ASCE)IR.1943-4774.0001646
dc.identifier.urihttps://hdl.handle.net/11501/1233
dc.identifier.volume148
dc.identifier.wosWOS:000742413100003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhasemlounia, Redvan
dc.institutionauthorid0000-0003-1796-4562
dc.language.isoen
dc.publisherAmerican Society of Civil Engineers (ASCE)
dc.relation.ispartofJournal of Irrigation and Drainage Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFlow Measurement
dc.subjectDischarge Coefficient of the SCVW
dc.subjectExperimental Model
dc.subjectArtificial Intelligence (AI) Methods
dc.subjectSupport Vector Regression
dc.subjectOptimization
dc.subjectPrediction
dc.subjectAlgorithm
dc.subjectNetwork
dc.subjectFlow
dc.subjectSimulation
dc.subjectCapacity
dc.subjectColony
dc.subjectNappe
dc.titleApplication of AI approaches to estimate discharge coefficient of novel kind of sharp-crested v-notch weirs
dc.typeArticle

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