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dc.contributor.authorHaddadchi, Arman
dc.contributor.authorMovahedi, Neshat
dc.contributor.authorVahidi, Elham
dc.contributor.authorOmid, Mohammad Hossein
dc.contributor.authorDehghani, Amir Ahmad
dc.date.accessioned2017-05-03T16:10:49Z
dc.date.available2017-05-03T16:10:49Z
dc.date.issued2013
dc.date.modified2014-02-20T22:49:42Z
dc.identifier.issn10016058
dc.identifier.doi10.1016/S1001-6058(11)60385-6
dc.identifier.urihttp://hdl.handle.net/10072/56991
dc.description.abstractAccurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofpagefrom459
dc.relation.ispartofpageto470
dc.relation.ispartofissue3
dc.relation.ispartofjournalJournal of Hydrodynamics
dc.relation.ispartofvolume25
dc.rights.retentionY
dc.subject.fieldofresearchEnvironmental Engineering Modelling
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode090702
dc.subject.fieldofresearchcode09
dc.titleEvaluation of suspended load transport rate using transport formulas and artificial neural network models (Case study: Chelchay Catchment)
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Environment
gro.date.issued2013
gro.hasfulltextNo Full Text
gro.griffith.authorHaddadchi, Arman


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