Multireservoir Modeling with Dynamic Programming and Neural Networks

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...

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Bibliographic Details
Published in:Journal of water resources planning and management 2001-04, Vol.127 (2), p.89-98
Main Authors: Chandramouli, V, Raman, H
Format: Article
Language:English
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TECHNICAL PAPERS
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Summary: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.
ISSN:0733-9496
1943-5452
DOI:10.1061/(ASCE)0733-9496(2001)127:2(89)