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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/68762
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dc.contributor.authorFisher, A.-
dc.contributor.authorGreen, D.-
dc.contributor.authorMetcalfe, A.-
dc.date.issued2012-
dc.identifier.citationAnnals of Operations Research, 2012; 199(1):215-224-
dc.identifier.issn0254-5330-
dc.identifier.issn1572-9338-
dc.identifier.urihttp://hdl.handle.net/2440/68762-
dc.description.abstractA reservoir in south east Queensland can supply irrigators, industry or domestic users. Stochastic inflow is modelled by a hidden state Markov chain, with three hidden states corresponding to prevailing climatic conditions. A stochastic dynamic program that relies on estimation of the hidden state is implemented. The optimal decisions are compared with those obtained if the hidden state Markov chain model is replaced with a model that relies on the Southern Oscillation Index to define prevailing climatic conditions.-
dc.description.statementofresponsibilityAiden Fisher, David Green, Andrew Metcalfe-
dc.language.isoen-
dc.publisherKluwer Academic Publishers-
dc.rights© Springer Science+Business Media, LLC 2011-
dc.source.urihttp://dx.doi.org/10.1007/s10479-011-0992-2-
dc.subjectHidden Markov model-
dc.subjectHidden state Markov decision process-
dc.subjectReservoir operation-
dc.titleModelling of hydrological persistence for hidden state Markov decision processes-
dc.typeJournal article-
dc.identifier.doi10.1007/s10479-011-0992-2-
pubs.publication-statusPublished-
dc.identifier.orcidMetcalfe, A. [0000-0002-7680-3577]-
Appears in Collections:Aurora harvest
Environment Institute publications
Mathematical Sciences publications

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