Modelling of hydrological persistence for hidden state Markov decision processes

Modelling of hydrological persistence for hidden state Markov decision processes

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Article ID: iaor20125553
Volume: 199
Issue: 1
Start Page Number: 215
End Page Number: 224
Publication Date: Oct 2012
Journal: Annals of Operations Research
Authors: , ,
Keywords: water, stochastic processes
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.

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