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

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Abstract

The present study evaluates the predictive accuracy of the feed forward backpropagation artificial neural network (BP) in evapotranspiration forecasting from temperature data basis in Dédougou 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.

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Acknowledgements

The authors acknowledge the Ministry of Agriculture, Hydraulic and Fisheries Resources of Burkina Faso for advising, collecting, and providing the data used in the study. The financial support provided by the International Cooperation and Development Fund (Taiwan ICDF) and NSC101-2625-M-020-003 is highly appreciated.

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Authors and Affiliations

  1. Ministry of Research and Scientific Innovation, INERA-Farako-ba, BoboDioulasso, Burkina Faso, Africa

    S TRAORE

  2. African Policy Center, United Nations Economic Commission for Africa (UNECA), Addis-Ababa, Ethiopia

    S TRAORE

  3. Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan, 91201, R.O.C.

    Y M WANG & W G CHUNG

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  1. S TRAORE
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  2. Y M WANG
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  3. W G CHUNG
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Correspondence to Y M WANG.

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TRAORE, S., WANG, Y.M. & CHUNG, W.G. Predictive accuracy of backpropagation neural network methodology in evapotranspiration forecasting in Dédougou region, western Burkina Faso. J Earth Syst Sci 123, 307–318 (2014). https://doi.org/10.1007/s12040-013-0398-4

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  • DOI: https://doi.org/10.1007/s12040-013-0398-4

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