IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v21y2007i6p933-945.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-006-9065-8
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11269-006-9065-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bonfante, A. & Monaco, E. & Manna, P. & De Mascellis, R. & Basile, A. & Buonanno, M. & Cantilena, G. & Esposito, A. & Tedeschi, A. & De Michele, C. & Belfiore, O. & Catapano, I. & Ludeno, G. & Salinas, 2019. "LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study," Agricultural Systems, Elsevier, vol. 176(C).
    2. Rui M. S. Pereira & Sofia Lopes & Amélia Caldeira & Victor Fonte, 2018. "Optimized Planning of Different Crops in a Field Using Optimal Control in Portugal," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    3. R. Rai & S. Sarkar & V. Singh, 2009. "Evaluation of the Adequacy of Statistical Distribution Functions for Deriving Unit Hydrograph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 899-929, March.
    4. Parolo, Gilberto & Ferrarini, Alessandro & Rossi, Graziano, 2009. "Optimization of tourism impacts within protected areas by means of genetic algorithms," Ecological Modelling, Elsevier, vol. 220(8), pages 1138-1147.
    5. Ojeda-Bustamante, Waldo & Gonzalez-Camacho, Juan Manuel & Sifuentes-Ibarra, Ernesto & Isidro, Esteban & Rendon-Pimentel, Luis, 2007. "Using spatial information systems to improve water management in Mexico," Agricultural Water Management, Elsevier, vol. 89(1-2), pages 81-88, April.
    6. Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2016. "Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model," Agricultural Water Management, Elsevier, vol. 178(C), pages 76-88.
    7. Hassan-Esfahani, Leila & Torres-Rua, Alfonso & McKee, Mac, 2015. "Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data," Agricultural Water Management, Elsevier, vol. 153(C), pages 42-50.
    8. Hamideh Noory & Mona Deyhool & Farhad Mirzaei, 2019. "A Simulation-Optimization Model for Conjunctive Use of Canal and Pond in Irrigating Paddy Fields," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1053-1068, February.
    9. S. Dutta & B.C. Sahoo & Rajashree Mishra & S. Acharya, 2016. "Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4097-4123, September.
    10. Dariane, A.B. & Ghasemi, M. & Karami, F. & Azaranfar, A. & Hatami, S., 2021. "Crop pattern optimization in a multi-reservoir system by combining many-objective and social choice methods," Agricultural Water Management, Elsevier, vol. 257(C).
    11. A. Vasan & Komaragiri Raju, 2007. "Application of Differential Evolution for Irrigation Planning: An Indian Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(8), pages 1393-1407, August.
    12. Mateos, Luciano & Lopez-Cortijo, Ignacio & Sagardoy, Juan A., 2002. "SIMIS: the FAO decision support system for irrigation scheme management," Agricultural Water Management, Elsevier, vol. 56(3), pages 193-206, August.
    13. Shanshan Guo & Fan Zhang & Chenglong Zhang & Chunjiang An & Sufen Wang & Ping Guo, 2018. "A Multi-Objective Hierarchical Model for Irrigation Scheduling in the Complex Canal System," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    14. K. Srinivasa Raju & D. Nagesh Kumar, 2004. "Irrigation Planning using Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 163-176, April.
    15. A. Garudkar & A. Rastogi & T. Eldho & S. Gorantiwar, 2011. "Optimal Reservoir Release Policy Considering Heterogeneity of Command Area by Elitist Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3863-3881, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:21:y:2007:i:6:p:933-945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.