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Simulation with RBF Neural Network Model for Reservoir Operation Rules

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  • Yi-min Wang
  • Jian-xia Chang
  • Qiang Huang

Abstract

Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particle swarm optimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:11:p:2597-2610
    DOI: 10.1007/s11269-009-9569-0
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    References listed on IDEAS

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    1. Raveendra Rai & B. Mathur, 2008. "Event-based Sediment Yield Modeling using Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 423-441, April.
    2. Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
    3. V. Chandramouli & Paresh Deka, 2005. "Neural Network Based Decision Support Model for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 447-464, August.
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    2. Lin She & Xue-yi You, 2019. "A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3143-3153, July.
    3. Guang Yang & Shenglian Guo & Pan Liu & Xiaofeng Liu & Jiabo Yin, 2020. "Heuristic Input Variable Selection in Multi-Objective Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 617-636, January.
    4. Sabah Fayaed & Ahmed El-Shafie & Othman Jaafar, 2013. "Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3679-3696, August.
    5. Leila Ostadrahimi & Miguel Mariño & Abbas Afshar, 2012. "Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 407-427, January.
    6. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.
    7. Ali Assani & Raphaëlle Landry & Jonathan Daigle & Alain Chalifour, 2011. "Reservoirs Effects on the Interannual Variability of Winter and Spring Streamflow in the St-Maurice River Watershed (Quebec, Canada)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3661-3675, November.
    8. Sharad Patel & A. K. Rastogi, 2017. "Meshfree Multiquadric Solution for Real Field Large Heterogeneous Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2869-2884, July.
    9. Yousif H. Al-Aqeeli & Omar M. A Mahmood Agha, 2020. "Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3099-3112, August.
    10. Inkyung Min & Nakyung Lee & Sanha Kim & Yelim Bang & Juyeon Jang & Kichul Jung & Daeryong Park, 2024. "An Improved Aggregation–Decomposition Optimization Approach for Ecological Flow Supply in Parallel Reservoir Systems," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
    11. V. Gholami & M. R. Khaleghi & S. Pirasteh & Martijn J. Booij, 2022. "Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 451-469, January.

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