Artificial Neural Networks in Hydrology
Artificial Neural Networks in Hydrology
Generalized regression neural network in modelling river sediment yield
Advances in Engineering Software
Determination of a reference model for estimating evapotranspiration in Burkina Faso
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Using artificial neural networks for modeling suspended sediment concentration
MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
A climatic data information model for computing crop yields in Burkina Faso
MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
WSEAS Transactions on Computers
Monitoring event-based suspended sediment concentration by artificial neural network models
WSEAS Transactions on Computers
A neuro-fuzzy model for function point calibration
WSEAS Transactions on Information Science and Applications
Computing and modeling for crop yields in Burkina Faso based on climatic data information
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Computers
Municipal revenue prediction by ensembles of neural networks and support vector machines
WSEAS Transactions on Computers
An automatic water management system for large-scale rice paddy fields
WSEAS Transactions on Systems and Control
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The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference evapotranspiration (ETo) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country such as Burkina Faso. ETo has been widely used for agricultural water management. Its accurate estimation is vitally important for computerizing crop water balance analysis. Therefore, a previous study has developed a reference model for Burkina Faso (RMBF) for estimating the ETo by using only temperature as input in two production sites, Banfora and Ouagadougou. This paper investigates for the first time in the semiarid environment of Burkina Faso, the potential of using an artificial neural network (ANN) for estimating ETo with limited climatic data set. The ANN model employed in the study was the feed forward backpropagation (BP) type using maximum and minimum air temperature collected from 1996 to 2006. The result of BP was compared to the RMBF, Hargreaves (HRG) and Blaney-Criddle (BCR) which have been successfully used for ETo estimation where there is not sufficient data. Based on the results of this study, it revealed that the BP prediction showed a higher accuracy than RMBF, HRG and BCR. The feed forward backpropagation algorithm could be potentially employed successfully to estimate ETo in semiarid zone.