Water distribution under trickle irrigation predicted using artificial neural networks

N. Lazarovitch, M. Poulton, A. Furman, A. W. Warrick

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


An artificial neural network (ANN) technology is presented as an alternative to physical-based modeling of subsurface water distribution from trickle emitters. Three options are explored to prepare input-output functional relations from a database created using a numerical model (HYDRUS-2D). From the database the feasibility and advantages of the three alternative options are evaluated: water-content at defined coordinates, moment analysis describing the shape of the plume, and coordinates of individual water-content contours. The best option is determined in a way by the application objectives, but results suggest that prediction using moment analyses is probably the most versatile and robust and gives an adequate picture of the subsurface distribution. Of the other two options, the direct determination of the individual water contours was subjectively judged to be more successful than predicting the water content at given coordinates, at least in terms of describing the subsurface distribution. The results can be used to estimate subsurface water distribution for essentially any soil properties, initial conditions or flow rates for trickle sources.

Original languageEnglish
Pages (from-to)207-218
Number of pages12
JournalJournal of Engineering Mathematics
Issue number2
StatePublished - 8 Apr 2009


  • Artificial neural networks
  • Drip irrigation
  • Spatial moments
  • Water flow

ASJC Scopus subject areas

  • General Mathematics
  • General Engineering


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