Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach

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dc.contributor.authorUnes, Fatih
dc.date.accessioned2024-09-18T20:02:40Z
dc.date.available2024-09-18T20:02:40Z
dc.date.issued2010
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractExperimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi-linear regression (MLR), and two-dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two-dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k-epsilon turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.en_US
dc.identifier.doi10.1002/clen.200900238
dc.identifier.endpage308en_US
dc.identifier.issn1863-0650
dc.identifier.issn1863-0669
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-77949948136en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage296en_US
dc.identifier.urihttps://doi.org/10.1002/clen.200900238
dc.identifier.urihttps://hdl.handle.net/20.500.12483/7955
dc.identifier.volume38en_US
dc.identifier.wosWOS:000276322000012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofClean-Soil Air Wateren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectDamen_US
dc.subjectDensity flowen_US
dc.subjectMathematical modelen_US
dc.subjectPlunging depthen_US
dc.titlePrediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approachen_US
dc.typeArticleen_US

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