Optimization of Operation Regimes of Irrigation Canals Using Genetic Algorithms

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

The paper focuses on the problem of irrigation canals operation regimes optimization which is important for minimizing operating expenses and ensuring stable water supply for agriculture. Regarding the complexity of the optimization problem we propose to solve it using genetic algorithm that searches for per-hour pumping station units’ operation regimes, their pumping rates and the heights of controllable weirs and gate structures that should guarantee water levels and flow velocities needed by farmers. As an underlying direct problem we use an initial-boundary value problem for one-dimensional Saint-Venant equations system discretized by finite difference scheme. The algorithms of direct and optimization problems solution was applied to model water flow and determine optimal water supply rates for North Crimean canal.

Keywords

Saint-Venant equation Inverse problems Genetic algorithms Irrigation systems 

References

  1. 1.
    Barbosa de Oliveira, J., Pinho, T.M., Coelho, J.P., Boaventura-Cunha, J., Oliveira P.M.: Optimized fractional order sliding mode controller for water level in irrigation canal pool. IFAC-PapersOnLine 50(1), 7663–7668 (2017)CrossRefGoogle Scholar
  2. 2.
    Ding, Z., Wang, C.: Research on canal system operation based on controlled volume method. Int. J. Intell. Syst. Appl. (IJISA) 1(1), 19–29 (2009).  https://doi.org/10.5815/ijisa.2009.01.03CrossRefGoogle Scholar
  3. 3.
    Lozano, D., Arranja, C., Rijo, M., Mateos, L.: Simulation of automatic control of an irrigation canal. Agric. Water Manag. 97(1), 91–100 (2010)CrossRefGoogle Scholar
  4. 4.
    Srinivasa Raju, K., Nagesh Kumar, D.: Irrigation planning using genetic algorithms. Water Resour. Manag. 18(2), 163–176 (2004)CrossRefGoogle Scholar
  5. 5.
    Mathur, Y.P., Sharma, G., Pawde, A.W.: Optimal operation scheduling of irrigation canals using genetic algorithm. Int. J. Recent Trends Eng. 1(6), 11–15 (2009)Google Scholar
  6. 6.
    Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  7. 7.
    Umbarkar, A.J., Joshi, M.S., Sheth, P.D.: Dual population genetic algorithm for solving constrained optimization problems. Int. J. Intell. Syst. Appl. (IJISA) 7(2), 34–40 (2015).  https://doi.org/10.5815/ijisa.2015.02.05CrossRefGoogle Scholar
  8. 8.
    Seshadri Sastry, K., Prasad Babu, M.S.: Adaptive population sizing genetic algorithm assisted maximum likelihood detection of OFDM symbols in the presence of nonlinear distortions. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 5(7), 58–65 (2013).  https://doi.org/10.5815/ijcnis.2013.07.07CrossRefGoogle Scholar
  9. 9.
    Boeira, J.N.R.: The effects of “Preferentialism” on a genetic algorithm population over elitism and regular development in a binary F6 fitness function. Int. J. Intell. Syst. Appl. (IJISA) 8(9), 38–46 (2016).  https://doi.org/10.5815/ijisa.2016.09.05CrossRefGoogle Scholar
  10. 10.
    Zhu, Y., Qin, D., Zhu,Y., Cao, X.: Genetic algorithm combination of boolean constraint programming for solving course of action optimization in influence nets. Int. J. Intell. Syst. Appl. (IJISA), 3(4), 1–7 (2011)CrossRefGoogle Scholar
  11. 11.
    Malaterre, P.O., Baume, J.P.: Modeling and regulation of irrigation canals: existing applications and ongoing researches. In: 1998 IEEE International Conference on Systems Man and Cybernetics, vol. 4, pp. 3850–3855 (1998)Google Scholar
  12. 12.
    Meselhe, E.A., Sotiropoulos, F., Holly, F.H.: Numerical simulation of transcritical flow in open channels. ASCE J. Hydr. Eng. 123(9), 774–783 (1997)CrossRefGoogle Scholar
  13. 13.
    Agu, C.E., Elseth, G., Lie, B.: Simulation of transcritical flow in hydraulic structures. In: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), 7–9 October 2015, pp. 369–375. Linköping University, Sweden (2015)Google Scholar
  14. 14.
    Brodtkorb, A.R., Hagen, T.R., Lie, K.A., et al.: Simulation and visualization of the Saint-Venant system using GPUs. Comput. Vis. Sci. 13, 341–353 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Abedini, M.J., Hashemi, M.R.: Effect of convective term suppression in numerical simulation of trans-critical open channel flow. Iran. J. Sci. Technol. Trans. B Eng. 30(1), 85–96 (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.VM Glushkov Institute of Cybernetics of NAS of UkraineKyivUkraine
  2. 2.Institute of Water Problems and Land Reclamation of NAAS of UkraineKyivUkraine

Personalised recommendations