Simulation with RBF Neural Network Model for Reservoir Operation Rules

Yi-min Wang, Jian-xia Chang () and Qiang Huang

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2010, vol. 24, issue 11, 2597-2610

Abstract: Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particle swarm optimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study. Copyright Springer Science+Business Media B.V. 2010

Keywords: Reservoirs operation rules; Simulation with RBF network model; Particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s11269-009-9569-0

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