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Neural Network Based Decision Support Model for Optimal Reservoir Operation

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  • V. Chandramouli
  • Paresh Deka

Abstract

A decision support model (DSM) has been developed using the artificial neural networks (ANN) for optimal operation of a reservoir in south India. The DSM developed is a combination of a rule based expert system and ANN models, which are trained using the results from deterministic single reservoir optimisation algorithm. The developed DSM is also flexible to use multiple linear regression equations instead of trained neural network models for different time periods. A new approach is tried with the DSM based on trained neural network models, which use real time data of previous time periods for deciding operating policies. The developed DSM based on ANN outperforms the regression based approach. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:19:y:2005:i:4:p:447-464
    DOI: 10.1007/s11269-005-3276-2
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    References listed on IDEAS

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    1. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
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    Cited by:

    1. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    2. Qiao-feng Tan & Guo-hua Fang & Xin Wen & Xiao-hui Lei & Xu Wang & Chao Wang & Yi Ji, 2020. "Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1589-1607, March.
    3. S. Mohan & N. Ramsundram, 2016. "Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3315-3330, August.
    4. Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2008. "Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 757-774, June.
    5. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    6. Xiaoling Ding & Xiaocong Mo & Jianzhong Zhou & Sheng Bi & Benjun Jia & Xiang Liao, 2021. "Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 645-660, January.
    7. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    8. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.
    9. 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.
    10. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    11. Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    12. 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.
    13. Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    14. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    15. 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.
    16. Tan, Qiao-feng & Lei, Xiao-hui & Wen, Xin & Fang, Guo-hua & Wang, Xu & Wang, Chao & Ji, Yi & Huang, Xian-feng, 2019. "Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage," Energy, Elsevier, vol. 183(C), pages 670-682.
    17. Panuwat Pinthong & Ashim Das Gupta & Mukand Babel & Sutat Weesakul, 2009. "Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 697-720, March.

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