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Modelling of hydrological persistence for hidden state Markov decision processes

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Listed:
  • 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. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Aiden Fisher & David Green & Andrew Metcalfe, 2012. "Modelling of hydrological persistence for hidden state Markov decision processes," Annals of Operations Research, Springer, vol. 199(1), pages 215-224, October.
    • Handle: RePEc:spr:annopr:v:199:y:2012:i:1:p:215-224:10.1007/s10479-011-0992-2
    DOI: 10.1007/s10479-011-0992-2
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    References listed on IDEAS

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    1. Erhan Bayraktar & Michael Ludkovski, 2010. "Inventory management with partially observed nonstationary demand," Annals of Operations Research, Springer, vol. 176(1), pages 7-39, April.
    2. I. David & L. Friedman & Z. Sinuany‐Stern, 1999. "A simple suboptimal algorithm for system maintenanceunder partial observability," Annals of Operations Research, Springer, vol. 91(0), pages 25-40, January.
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