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ESTIMATION OF EVAPOTRANSPIRATION FOR WHEAT CROP USING ARTIFICIAL NEURAL NETWORK
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006 701P0606.(doi:10.13031/2013.21891)
Authors: Sita Ram Bhakar, Santosh Ojha, Raj Vir Singh and Aasif Ansari
Keywords: NEURAL NETWORKS, EVAPOTRANSPIRATION, CROP, AND WHEAT
The study has been undertaken to investigate the utility of artificial neural networks (ANNs) for
comparison of daily reference evapotranspiration (ET0) estimated by Penman-Monteith (PM) method and
that of estimated by ANNs during growing season of wheat crop. Feed forward network has been used for
prediction of ET0 using resilient back-propagation method. For the purpose of the study, daily
meteorological observations such as minimum and maximum temperature, minimum and maximum relative
humidity, wind speed and solar radiation for the period of November 21, 1997 to March 2, 1998 were used
as input and ET0 estimated by Penman -Monteith method for growing season of wheat crop as output.