Multireservoir Modeling with Dynamic Programming and Neural Networks
Publication: Journal of Water Resources Planning and Management
Volume 127, Issue 2
Abstract
For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feedforward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar Project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming-neural network model approach. The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 127 • Issue 2 • April 2001
Pages: 89 - 98
History
Received: Feb 24, 1999
Published online: Apr 1, 2001
Published in print: Apr 2001
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