Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.


Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  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

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.

The comparisons were made between ANNs estimated ET0 and ET0 estimated using PM method. The correlation coefficient between actual and predicted ET0 during training of ET0 for growing season of wheat crop was found to be 0.990 which was found to be significant at 5 % level.

The networks were also used for computation of crop evapotranspiration (ETc). During training of ETc, crop coefficient values estimated by quadratic method have been taken as input to the network along with meteorological parameters and ETc estimated using crop coefficient approach and that of measured by lysimeter as output separately. The crop evapotranspiration estimated by ANNs were compared with ETc estimated by crop coefficient approach and that of evapotranspiration measured by lysimeter. The correlation coefficients during training of ETc of wheat crop were found to be 0.994 and 0.915 respectively which were also found significant at 5 % level. Based on these comparisons, it can be concluded that the ANN models is suitable for prediction of ET0 and ETc.

(Download PDF)    (Export to EndNotes)