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Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

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  • C. Sivapragasam
  • G. Vasudevan
  • P. Vincent

Abstract

In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization. Copyright Springer Science+Business Media B.V. 2007

Suggested Citation

  • C. Sivapragasam & G. Vasudevan & P. Vincent, 2007. "Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 933-945, June.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:6:p:933-945
    DOI: 10.1007/s11269-006-9065-8
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    References listed on IDEAS

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    1. Kuo, Sheng-Feng & Merkley, Gary P. & Liu, Chen-Wuing, 2000. "Decision support for irrigation project planning using a genetic algorithm," Agricultural Water Management, Elsevier, vol. 45(3), pages 243-266, August.
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    Cited by:

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    2. Ali Arefinia & Omid Bozorg-Haddad & Khaled Ahmadaali & Javad Bazrafshan & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8378-8396, June.
    3. Andre Ferreira & Ramesh Teegavarapu, 2012. "Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(3), pages 707-732, February.
    4. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Application of Fixed Length Gene Genetic Programming (FLGGP) in Hydropower Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3357-3370, July.

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