Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
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VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 129, Issue 3
Abstract
This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop. Six RBF networks, each using varied input combinations of climatic variables, have been trained and tested. The model estimates are compared with measured lysimeter ET. The results of the study clearly demonstrate the proficiency of the ANN method in estimating the ET. The analyses suggest that the crop ET could be computed from air temperature using the ANN approach. However, the present study used a single crop data for a limited period, therefore further studies using more crops as well as weather conditions may be required to strengthen these conclusions.
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Copyright © 2003 American Society of Civil Engineers.
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Received: Dec 6, 2000
Accepted: Jul 3, 2002
Published online: May 15, 2003
Published in print: Jun 2003
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