Sampling/stochastic dynamic programming for optimal operation of multi-purpose reservoirs using artificial neural network-based ensemble streamflow predictions

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 wate...

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Bibliographic Details
Published in:Journal of hydroinformatics Vol. 16; no. 4; pp. 907 - 921
Main Authors: Anvari, Sedigheh, Mousavi, S. Jamshid, Morid, Saeed
Format: Journal Article
Language:English
Published: London IWA Publishing 01.01.2014
Subjects:
Agricultural products
Arid zones
Artificial neural networks
Case studies
Computer simulation
Dynamic programming
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hydrology
Hydrology. Hydrogeology
Inflow
Mathematical models
Multipurpose reservoirs
Neural networks
Optimization
Policies
Real time operation
Reservoir operation
Reservoirs
Sampling
Semi arid areas
Semiarid zones
Stream discharge
Stream flow
Surface water
Water inflow
Water reservoirs
Water resources
Middle East
Iran
Asia
models
water policy
neural networks
reservoirs
arid environment
digital simulation
case studies
classification
surface water
water resources
stochastic models
optimization
water resource management
prediction
uncertainties
streamflow
ISSN:1464-7141, 1465-1734
Online Access:Get full text
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Summary: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.
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ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2013.236