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|Title:||Modelling of hydrological persistence for hidden state Markov decision processes|
|Citation:||Annals of Operations Research, 2012; 199(1):215-224|
|Publisher:||Kluwer Academic Publishers|
|Aiden Fisher, David Green, Andrew Metcalfe|
|Abstract:||A 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.|
|Keywords:||Hidden Markov model|
Hidden state Markov decision process
|Rights:||© Springer Science+Business Media, LLC 2011|
|Appears in Collections:||Aurora harvest|
Environment Institute publications
Mathematical Sciences publications
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