Browse

Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction

DC Field Value Language
dc.contributor.authorJeong, Dae-Il-
dc.contributor.authorKim, Young-Oh-
dc.date.accessioned2010-06-08T04:57:33Z-
dc.date.available2010-06-08T04:57:33Z-
dc.date.issued2005-
dc.identifier.citationHydrological Processes 19: 3819-3835en
dc.identifier.issn0885-6087-
dc.identifier.urihttps://hdl.handle.net/10371/67631-
dc.description.abstractPrevious ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall-runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artificial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall-runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inflows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root-mean-square error. The overall results showed that the ENN performed the best among the three rainfall-runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half-Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall-runoff modelling than TANK or an SNN. Copyrighten
dc.language.isoenen
dc.publisherWiley-Blackwellen
dc.subjectartificial neural networksen
dc.subjectensemble neural networken
dc.subjectensemble streamflow predictionen
dc.subjectprobabilistic forecastingen
dc.subjectrainfall-runoff modelen
dc.titleRainfall-runoff models using artificial neural networks for ensemble streamflow predictionen
dc.typeArticleen
dc.contributor.AlternativeAuthor정대일-
dc.contributor.AlternativeAuthor김영오-
dc.identifier.doi10.1002/hyp.5983-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)Journal Papers (저널논문_건설환경공학부)
Files in This Item:
There are no files associated with this item.
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse