Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2095
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dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorMoramarco, Tommaso-
dc.contributor.authorSingh, Vijay P.-
dc.date.accessioned2016-08-12T12:00:00Z
dc.date.available2016-08-12T12:00:00Z
dc.date.issued2007-07
dc.identifier.citationTayfur, G., Moramarco, T., and Singh, V. P. (2007). Predicting and forecasting flow discharge at sites receiving significant lateral inflow. Hydrological Processes, 21(14), 1848-1859. doi:10.1002/hyp.6320en_US
dc.identifier.issn0885-6087
dc.identifier.issn0885-6087-
dc.identifier.urihttp://doi.org/10.1002/hyp.6320
dc.identifier.urihttp://hdl.handle.net/11147/2095
dc.description.abstractTwo models, one linear and one non-linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non-linear model is based on a multilayer feed-forward back propagation (FFBP) artificial neural network (ANN) and uses flow-stage data measured at the upstream and downstream stations. ANN predicted the real-time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow-stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4-h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8-h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Inc.en_US
dc.relation.ispartofHydrological Processesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectFloodsen_US
dc.subjectFeed-forward back propagationen_US
dc.subjectFlood hydrographen_US
dc.subjectModified Muskingum methoden_US
dc.subjectForecastingen_US
dc.titlePredicting and forecasting flow discharge at sites receiving significant lateral inflowen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume21en_US
dc.identifier.issue14en_US
dc.identifier.startpage1848en_US
dc.identifier.endpage1859en_US
dc.identifier.wosWOS:000248234100006en_US
dc.identifier.scopus2-s2.0-34447334519en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1002/hyp.6320-
dc.relation.doi10.1002/hyp.6320en_US
dc.coverage.doi10.1002/hyp.6320en_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeArticle-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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