Optimal Irrigation Scheduling with Evolutionary Algorithms

  • Michael de Paly
  • Andreas Zell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)


Efficient irrigation is becoming a necessity in order to cope with the aggravating water shortage while simultaneously securing the increasing world population’s food supply. In this paper, we compare five Evolutionary Algorithms (real valued Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and two Evolution Strategy-based Algorithms) on the problem of optimal deficit irrigation. We also introduce three different constraint handling strategies that deal with the constraints which arise from the limited amount of irrigation water. We show that Differential Evolution and Particle Swarm Optimization are able to optimize irrigation schedules achieving results which are extremely close to the theoretical optimum.


Irrigation scheduling deficit irrigation evolutionary computation 


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  1. 1.
    Diouf, J.: Agriculture, food security and water: Towards a blue revolution, OECD Observer (2003)Google Scholar
  2. 2.
    Smith, M.: Cropwat: A computer program for irrigation planing and management, Tech. report, FAO Land and Water Development Division, Rome (1992)Google Scholar
  3. 3.
    Bras, R.L., Cordova, J.R.: Intraseasonal Water Allocation in Deficit Irrigation. Water Resources Research 17(4), 866–874 (1981)CrossRefGoogle Scholar
  4. 4.
    Rao, N.H., Sarma, P.B.S., Chander, S.: Irrigation Scheduling under a Limited Water Supply. Agricultural Water Management 15, 168–175 (1988)CrossRefGoogle Scholar
  5. 5.
    Rao, N.H., Sarma, P.B.S., Chander, S.: Optimal Multicrop Allocation of Seasonal and Intraseasonal Irrigation Water. Water Resources Research 26(4), 551–559 (1990)CrossRefGoogle Scholar
  6. 6.
    Sunantara, J.D., Ramirez, J.A.: Optimal Stochastic Multicrop Seasonal and Intraseasonal Irrigation Control. Journal of Water Ressource Planing and Management 123(1), 39–48 (1997)CrossRefGoogle Scholar
  7. 7.
    Schütze, N., Wöhling, T., de Paly, M., Schmitz, G.: Global optimization of deficit irrigation systems using evolutionary algorithms. In: Proceedings of the XVI International Conference on Computational Methods in Water Resources, Copenhagen, Denmark (2006)Google Scholar
  8. 8.
    Schmitz, G., Wöhling, T., de Paly, M., Schütze, N.: Gain-p: A new strategy to increase furrow irrigation efficiency. Arabian Journal for Science and Engineering 32(1C), 103–114 (2007)Google Scholar
  9. 9.
    Rao, N.H.: Field test of a simple soil-water balance model for irrigated areas. Journal of Hydrology 91, 179–186 (1987)CrossRefGoogle Scholar
  10. 10.
    Doorenbos, J., Kassam, A.H.: Yield response to water. FAO Irrigation and Drainage Paper 33, FAO Rome (1979)Google Scholar
  11. 11.
    Streichert, F., Ulmer, H.: JavaEvA - A Java Framework for Evolutionary Algorithms. Center for Bioinformatics Tübingen, University of Tübingen, Technical Report WSI-2005-06 (2005),
  12. 12.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, New York (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michael de Paly
    • 1
  • Andreas Zell
    • 1
  1. 1.Center for Bioinformatics Tübingen (ZBIT)TübingenGermany

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