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dc.contributor.authorBahrami, H.
dc.contributor.authorEmamgholi Zadeh, S.
dc.coverage.spatialIranen_US
dc.coverage.spatialKaroon Riveren_US
dc.date.accessioned2018-11-11T09:03:04Z
dc.date.available2018-11-11T09:03:04Z
dc.date.issued2018
dc.identifier.issn2008-8965
dc.identifier.urihttp://hdl.handle.net/1834/14756
dc.description.abstractAccurate estimation of sediment concentrations in hydraulic sediment transport from different viewpoint such as sediment discharge estimation of river, selection of hydraulic structures and etc. are important. With respect to importance of this issue in this study for prediction of sediment concentration of Karun river multi-layer perceptron artificial neural network (ANN / MLP) was used. For this purpose 125 field data including bottom concentration, flow velocity, nearest distance from the beach, and the total depth of flow and flow depth was used. Three statistical metrics namely mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R^2 ) were used to evaluate the performance of ANN model. The result shows that MLP model with one hidden layer, Sigmoid transfer function and 5 neurons have best structure in the modeling of sediment concentration of Kroon River. The R^2 and RMSE value is equal to 0.953 and 63.37 mg/l in training stage and 0.752 and 203.02 mg/l in testing stage, respectively. Finally, the sensitive analysis also showed that the nearest distance from the beach and flow depth had the most and the least effect on the sediment concentration, respectively.en_US
dc.language.isofaen_US
dc.titlePrediction of suspended sediment distribution of Karoon River using artificial neural networken_US
dc.typeJournal Contributionen_US
dc.bibliographicCitation.issue2en_US
dc.bibliographicCitation.titleJournal of Marine Science and Technologyen_US
dc.bibliographicCitation.volume17en_US
dc.description.statusPublisheden_US
dc.format.pagerangepp.27-35en_US
dc.subject.asfaModelingen_US
dc.subject.asfaSediment concentrationen_US
dc.subject.asfaArtificial neural networksen_US
dc.subject.asfaDistributionen_US
dc.subject.asfaSedimenten_US
dc.type.refereedRefereeden_US
refterms.dateFOA2021-01-30T18:48:39Z


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