Modelling of hydrological persistence for hidden state Markov decision processes

被引：0
|
作者：
Aiden Fisher
David Green
Andrew Metcalfe
机构：
[1] University of Adelaide,
来源：
Annals of Operations Research | 2012年 / 199卷
关键词：
Hidden Markov model; Hidden state Markov decision process; Reservoir operation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：215 / 224
页数：9
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