US EPA

2673334 

Journal Article 

Predictive accuracy of backpropagation neural network methodology in evapotranspiration forecasting in Dedougou region, western Burkina Faso 

Tradre, S; Wang, YM; Chung, WG 

2014 

Yes 

Journal of Earth System Science
ISSN: 0253-4126
EISSN: 0973-774X 

123 

2 

307-318 

WOS:000333546500005 

The present study evaluates the predictive accuracy of the feed forward backpropagation artificial neural network (BP) in evapotranspiration forecasting from temperature data basis in Dedougou region located in western Burkina Faso, sub-Saharan Africa. BP accuracy is compared to the conventional Blaney-Criddle (BCR) and Reference Model developed for Burkina Faso (RMBF) by referring to the FAO56 Penman-Monteith (PM) as the standard method. Statistically, the models' accuracies were evaluated with the goodness-of-fit measures of root mean square error, mean absolute error and coefficient of determination between their estimated and PM observed values. From the statistical results, BP shows similar contour trends to PM, and performs better than the conventional methods in reference evapotranspiration (ET_ref) forecasting in the region. In poor data situation, BP based only on temperature data is much more preferred than the other alternative methods for ET_ref forecasting. Furthermore, it is noted that the BP network computing technique accuracy improves significantly with the addition of wind velocity into the network input set. Therefore, in the region, wind velocity is recommended to be incorporated into the BP model for high accuracy management purpose of irrigation water, which relies on accurate values of ET_ref. 

Temperature basis models; intelligent computing; irrigation management; sub-Saharan Africa