Comparison of estimated reference evapotranspiration by using neural networks in the Sahelian zone

  • Authors:

  • Seydou Traore;Yu-Min Wang;Tienfuan Kerh

  • Affiliations:

  • Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung, Taiwan;Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung, Taiwan;Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung, Taiwan

  • Venue:
  • ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
  • Year:
  • 2008

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Abstract

The irrigation management in Burkina Faso still faced to climatic data unavailability for estimating the ETo with the recommended Penman-Monteith (PM) equation. Recently, to overcome the climatic data unavailability difficulty, a reference model for Burkina Faso (RMBF) using only temperature as input has been developed for irrigation management purpose in two production sites, Banfora and Ouagadougou. In this study, four alternative methods to PM, including the generalized regression neural network (GRNN), RMBF, Hargreaves (HRG) and Blaney-Criddle (BCR) were employed to study their performances in three production sites, Dori, Bogande and Fada N'gourma. The minimum climatic data were set to the maximum and minimum air temperature as input variables collected from 1996 to 2006. The performance from GRNN was higher than RMBF, HRG and BCR. In addition, by the typical Sahelian climatic condition, the wind velocity has been found as a sensitive parameter in the ETo estimation. Obviously, using GRNN with limited climatic variables for ETo estimation is more reliable than the other alternative methods.