Abstract
Complexicity in reservoir operation poses serious challenges to water resources planners and managers. These challenges of water reservoir operation are illustrated using a simulation to aid the development of an optimal operation policy for dam and reservoir. To achieve this, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model were developed and tested at Sg. Langat Reservoir in Malaysia. The nonlinearity of the natural physical processes was a major problem in determining the simulation of the reservoir parameters (elevation, surface-area, storage). To overcome water shortages resulting from uncertainty, the SDP-ANN model was used to evaluate the input variable and the performance outcome of the Model were compared with the Stochastic Dynamic Programming integrated with auto-regression (SDP-AR) model. The objective function of the models was set to minimize the sum of squared deviation from the desired targeted supply. Comparison result on the performance between SDP-AR model policy with SDP-ANN model found that the SDP-ANN model is a reliable and resilience model with a lesser supply deficit. The study concludes that the SDP-ANN model performs better than the SDP-AR model in deriving an optimal operating policy for the reservoir.
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Acknowledgments
This research was supported by a research grant to the second author by Ministry of Higher Education, University Kebangsaan Malaysia, and project leader FRGS/1/2012/TK03/UKM/02/4. The authors would like to thank the Puncak Niaga (M) SDN BHD for providing the data for Sg. Langat reservoir.
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Fayaed, S.S., El-Shafie, A. & Jaafar, O. Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy. Water Resour Manage 27, 3679–3696 (2013). https://doi.org/10.1007/s11269-013-0373-5
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DOI: https://doi.org/10.1007/s11269-013-0373-5