A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network

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dc.contributor.authorHumphrey, G.
dc.contributor.authorGibbs, M.
dc.contributor.authorDandy, G.
dc.contributor.authorMaier, H.
dc.date.issued2016
dc.description.abstractAbstract not available
dc.description.statementofresponsibilityGreer B. Humphrey, Matthew S. Gibbs, Graeme C. Dandy, Holger R. Maier
dc.identifier.citationJournal of Hydrology, 2016; 540:623-640
dc.identifier.doi10.1016/j.jhydrol.2016.06.026
dc.identifier.issn0022-1694
dc.identifier.issn1879-2707
dc.identifier.orcidHumphrey, G. [0000-0001-7782-5463]
dc.identifier.orcidGibbs, M. [0000-0001-6653-8688]
dc.identifier.orcidDandy, G. [0000-0001-5846-7365]
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]
dc.identifier.urihttp://hdl.handle.net/2440/105969
dc.language.isoen
dc.publisherElsevier
dc.rights© 2016 Elsevier B.V. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.jhydrol.2016.06.026
dc.subjectMonthly streamflow forecasting; Bayesian artificial neural networks; Conceptual hydrological models; uncertainty; hybrid modelling; South Australia
dc.titleA hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network
dc.typeJournal article
pubs.publication-statusPublished

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