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Sampling/stochastic dynamic programming for optimal operation of multi-purpose reservoirs using artificial neural network-based ensemble streamflow predictions.
- Source :
-
Journal of Hydroinformatics . Jul2014, Vol. 16 Issue 4, p907-922. 15p. - Publication Year :
- 2014
-
Abstract
- Due to limited water resources and the increasing demand for agricultural products, it is significantly important to operate surface water reservoirs optimally, especially those located in arid and semi-arid regions. This paper investigates uncertainty-based optimal operation of a multi-purpose water reservoir system by using four optimization models. The models include dynamic programming (DP), stochastic DP (SDP) with inflow classification (SDP/Class), SDP with inflow scenarios (SDP/Scenario), and sampling SDP (SSDP) with historical scenarios (SSDP/Hist). The performance of the models was tested in Zayandeh-Rud Reservoir system in Iran by evaluating how their release policies perform in a simulation phase. While the SDP approaches were better than the DP approach, the SSDP/Hist model outperformed the other SDP models. We also assessed the effect of ensemble streamflow predictions (ESPs) that were generated by artificial neural networks on the performance of SSDP/Hist. Application of the models to the Zayandeh-Rud case study demonstrated that SSDP in combination with ESPs and the K-means technique, which was used to cluster a large number of ESPs, could be a promising approach for real-time reservoir operation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14647141
- Volume :
- 16
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Hydroinformatics
- Publication Type :
- Periodical
- Accession number :
- 97122268
- Full Text :
- https://doi.org/10.2166/hydro.2013.236