Developing optimal release policies of multipurpose reservoirs is very complex, especially for reservoirs within a stochastic environment. Existing techniques are limited in their ability to represent risks associated with deciding a release policy. The risk aspect of the decisions affects the design and operation of reservoirs. A decision-making model is presented that is capable of replicating the manner in which risks associated with reservoir release decisions are perceived, interpreted and compared by a decision-maker. The model is based on Neural Network (NN) theory. This decision-making model can be used with a Stochastic Dynamic Programming (SDP) approach to produce a NN-SDP model. The resulting integrated model allows the attitudes towards risk of a decision-maker to be considered explicitly in defining the optimal release policy. Clear differences in the policies generated from the basic SDP and the NN-SDP models are observed when examining the operation of Aswan High Dam (AHD). The NN-SDP model yields policies that are more reliable and resilient and less vulnerable than those obtained using the SDP model.
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Research Article|
February 01 2011
An integrated neural network stochastic dynamic programming model for optimizing the operation policy of Aswan High Dam
A. H. El-Shafie;
1Civil and Structural Engineering Department, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
E-mail: [email protected]
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M. S. El-Manadely
M. S. El-Manadely
2Hydraulic and Irrigation Department, Faculty of Engineering, Cairo University, P.O. Box 12613, Giza, Eygpt
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Hydrology Research (2011) 42 (1): 50–67.
Article history
Received:
April 05 2009
Accepted:
February 05 2010
Citation
A. H. El-Shafie, M. S. El-Manadely; An integrated neural network stochastic dynamic programming model for optimizing the operation policy of Aswan High Dam. Hydrology Research 1 February 2011; 42 (1): 50–67. doi: https://doi.org/10.2166/nh.2010.043
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