CONTROLO 2016 pp 275-284 | Cite as

Data Based Modeling of a Large Scale Water Delivery System

  • Marta FernandesEmail author
  • Paulo Oliveira
  • Susana Vieira
  • Luís Mendonça
  • João Lemos Nabais
  • Miguel Ayala Botto
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 402)


Water is a vital resource and the growing populations and economies around the globe are pushing its demand worldwide. Therefore, the water conveyance operation should be well managed and improved. This paper proposes the development of reliable models able to predict water levels of a real 24.4 km water delivery channel in real time. This is a difficult task because this is a time-delayed dynamical system distributed over a long distance with nonlinear characteristics and external perturbations. Artificial neural networks are used, which are a well-known modeling technique that has been applied to complex and nonlinear systems. Real data is used for the design and validation of the models. The model obtained has the ability to predict water levels along the channel with minimum error, which can result in significant reduction of wasted water when implementing an automatic controller.


Data based modeling Water delivery systems Artificial neural networks Nonlinear autoregressive exogenous model 



This work is supported by the Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA Pest-OE/EME/LA0022, and supported by the project PTDC/EMS-CRO/2042/2012. Susana Vieira acknowledges the support by the Program Investigador FCT (IF/00833/2014) from FCT, cofunded by the European Social Fund (ESF) through the Operational Program Human Potential (POPH).


  1. 1.
    Water, U.: The United Nations World Water Development Report 2014: Water and Energy. UNESCO, Paris (2014)Google Scholar
  2. 2.
    Malaterre, P., Baume, J.: Modeling and regulation of irrigation canals: existing applications and ongoing researches. In: IEEE International Conference on Systems Man and Cybernetics, vol. 4, pp. 3850–3855. Institute of Electrical Engineers Inc. (IEEE) (1998)Google Scholar
  3. 3.
    Zimbelman, D.D., Bedworth, D.D.: Computer control for irrigation-canal system. J. Irrig. Drainage Eng. 109(1), 43–59 (1983)CrossRefGoogle Scholar
  4. 4.
    Mareels, I., Weyer, E., Ooi, S.K., Cantoni, M., Li, Y., Nair, G.: Systems engineering for irrigation systems: successes and challenges. Annu. Rev. Control 29(2), 191–204 (2005)CrossRefGoogle Scholar
  5. 5.
    Weyer, E.: System identification of an open water channel. Control Eng. Pract. 9(12), 1289–1299 (2001)CrossRefGoogle Scholar
  6. 6.
    Weyer, E.: Control of irrigation channels. IEEE Trans. Control Syst. Technol. 16(4), 664–675 (2008)CrossRefGoogle Scholar
  7. 7.
    Eurén, K., Weyer, E.: System identification of open water channels with undershot and overshot gates. Control Eng. Pract. 15(7), 813–824 (2007)CrossRefGoogle Scholar
  8. 8.
    Karunanithi, N., Grenney, W.J., Whitley, D., Bovee, K.: Neural networks for river flow prediction. J. Comput. Civ. Eng. 8(2), 201–220 (1994)CrossRefGoogle Scholar
  9. 9.
    Vieira, S., Sousa, J., Durao, F.: Fuzzy modelling strategies applied to a column flotation process. Miner. Eng. 18(7), 725–729 (2005)CrossRefGoogle Scholar
  10. 10.
    Lourenço, J., Botto, M., et al.: Modular modeling for large scale canal networks. In: 10th Portuguese Conference on Automatic Control, Funchal, Portugal, pp. 347–352 (2012)Google Scholar
  11. 11.
    Nabais, J., Duarte, J., Botto, M., Rijo, M.: Flexible framework for modeling water conveyance networks (2011). In: 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2011), pp. 142–147, Noordwijkerhout, The Netherlands (2011)Google Scholar
  12. 12.
    Nabais, J.M.L.C., Botto, M.A.: Linear model for canal pools. In: 8th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2011, vol. 1, pp. 303–313, Noordwijkerhout, The Netherlands (2011)Google Scholar
  13. 13.
    Rivas-Perez, R., Feliu-Batlle, V., Castillo-Garcia, F., Linarez-Saez, A.: System identification for control of a main irrigation canal pool. In: Proceedings of the 17h International Federation of Automatic Control (IFAC) World Congress, Seoul, South Corea, vol. 17 Part 1 (2008)Google Scholar
  14. 14.
    Zhuan, X., Xia, X.: Models and control methodologies in open water flow dynamics: a survey. In: AFRICON 2007, pp. 1–7. IEEE (2007)Google Scholar
  15. 15.
    Sousa, J.M., Kaymak, U.: Fuzzy decision making in modeling and control, vol. 27. World Scientific (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Marta Fernandes
    • 1
    Email author
  • Paulo Oliveira
    • 1
  • Susana Vieira
    • 1
  • Luís Mendonça
    • 1
    • 2
  • João Lemos Nabais
    • 1
    • 3
  • Miguel Ayala Botto
    • 1
  1. 1.IDMEC, LAETA, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.Department of Marine EngineeringEscola Superior Náutica Infante D. HenriqueOeirasPortugal
  3. 3.School of Business AdministrationPolytechnical Institute of SetúbalSetúbalPortugal

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