Article ID: | iaor20023676 |
Country: | United States |
Volume: | 127 |
Issue: | 2 |
Start Page Number: | 89 |
End Page Number: | 98 |
Publication Date: | Mar 2001 |
Journal: | Journal of Water Resources Planning and Management ASCE |
Authors: | Chandramouli V., Raman H. |
Keywords: | programming: dynamic, 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 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.