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Stochastic modeling of Karasu River (Turkey) using the methods of artificial neural networks

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dc.contributor.authorCan, İbrahim, author
dc.contributor.authorYerdelen, Cahit, author
dc.contributor.authorKahya, Ercan, author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-02-04T20:45:35Z
dc.date.available2020-02-04T20:45:35Z
dc.date.issued2007
dc.description2007 annual AGU hydrology days was held at Colorado State University on March 19 - March 21, 2007.
dc.descriptionIncludes bibliographical references.
dc.description.abstractEngineering project design, environmental impact analysis and water resources problems, particularly hydrological simulation and forecasting often require the estimation of streamflow. Simulation and forecasting techniques of streamflow time series have been applied widely for determining the dimensions of hydraulic works for risk assessment in urban water supply systems and irrigation, optimal operation of reservoir systems, optimization hydroelectric production, and so on. The present study uses two different artificial neural network (ANN) approaches for the stochastic modeling of the monthly mean streamflow of Karasu River located in the Upper Euphrates River Basin in the Eastern Anatolia part of Turkey. The ANN approaches used in this study are well known one-hidden layer feed forward back propagation (FFBP) architecture trained with the error back propagation and a variant of radial basis networks called Generalized Regression Neural Networks (GRNN). The study shows that the GRNN performs very well in the prediction of the hydrological time series. The performance of the GRNN is also found to be comparable with that of FFBP.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/200688
dc.identifier.urihttp://dx.doi.org/10.25675/10217/200688
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofHydrology Days
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleStochastic modeling of Karasu River (Turkey) using the methods of artificial neural networks
dc.title.alternativeHydrology days 2007
dc.title.alternativeAGU hydrology days 2007
dc.typeText

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