Dam management with imperfect models: bayesian model averaging and neural network control

Darwen, Paul J. (2012) Dam management with imperfect models: bayesian model averaging and neural network control. In: Proceedings of 8th International Conference on Intelligent Computing (304), pp. 360-366. From: ICIC 2012, 25-29 July 2012, Huangshan, China.

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Abstract

Dam management is a controversial control problem for two reasons. Firstly, models are (by definition) crudely simplified versions of reality. Secondly, historical rainfall data is limited and noisy. As a result, there is no agreement on the "best" control policy for running a dam. Bayesian model averaging is theoretically a good way to cope with these difficulties, but in practice it degrades under two approximations: discretizing the parameter space, and excluding models with a low probability of being correct. This paper explores the practical aspects of how Bayesian model averaging with a neural network controller can improve dam management and flood control.

Item ID: 22364
Item Type: Conference Item (Refereed Research Paper - E1)
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ISBN: 978-3-642-31836-8
Date Deposited: 21 Jan 2013 22:31
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 80%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling @ 20%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 80%
96 ENVIRONMENT > 9609 Land and Water Management > 960913 Water Allocation and Quantification @ 20%
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