Neuro-Drip: Estimation of subsurface wetting patterns for drip irrigation using neural networks

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

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


Design of efficient drip irrigation systems requires information about the subsurface water distribution of added water during and after infiltration. Further, this information should be readily accessible to design engineers and practitioners. Neuro-Drip combines an artificial neural network (ANN) with a statistical description of the spatio-temporal distribution of the added water from a single drip emitter to provide easily accessible, rapid illustrations of the spatial and temporal subsurface wetting patterns. In this approach, the ANN is an approximator of a flow system. The ANN is trained using close to 1,000 numerical simulations of infiltration. Moment analysis is used to encapsulate the spatial distribution of water content. In practice, the user provides soil hydraulic properties and discharge rate; the ANN is then used to estimate the depth to the center of mass of the added water, and the vertical and radial spreading around the center of mass; finally, this statistical description of the added water is used to visualize the fate of the added water during and after the infiltration event.

Original languageEnglish (US)
Pages (from-to)535-544
Number of pages10
JournalIrrigation Science
Issue number6
StatePublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Water Science and Technology
  • Soil Science


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