Data Based Modeling of a Large Scale Water Delivery System
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.
KeywordsData 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.Water, U.: The United Nations World Water Development Report 2014: Water and Energy. UNESCO, Paris (2014)Google Scholar
- 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
- 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.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.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.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.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.Sousa, J.M., Kaymak, U.: Fuzzy decision making in modeling and control, vol. 27. World Scientific (2002)Google Scholar