Water distribution under trickle irrigation predicted using artificial neural networks

TY - JOUR

T1 - Water distribution under trickle irrigation predicted using artificial neural networks

AU - Lazarovitch, N.

AU - Poulton, M.

AU - Furman, A.

AU - Warrick, A. W.

N1 - Funding Information: Acknowledgments This work was supported by The United States–Israel Binational Agricultural Research and Development fund (BARD), Project Grant Agreement No. US-3662-05R.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Artificial neural networks

KW - Drip irrigation

KW - Spatial moments

KW - Water flow

UR - http://www.scopus.com/inward/record.url?scp=67349180703&partnerID=8YFLogxK

U2 - 10.1007/s10665-009-9282-2

DO - 10.1007/s10665-009-9282-2

M3 - 文章

AN - SCOPUS:67349180703

SN - 0022-0833

VL - 64

SP - 207

EP - 218

JO - Journal of Engineering Mathematics

JF - Journal of Engineering Mathematics

IS - 2

ER -