Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone

The major problem when dealing with modeling evapotranspiration process is its nonlinear dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) estimation models in rich and poor data situations. Thus, the well-known Penman–Monteith (PM) model always performs the highes...

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Vydané v:	Agricultural water management Ročník 97; číslo 5; s. 707 - 714
Hlavní autori:	Traore, Seydou, Wang, Yu-Min, Kerh, Tienfuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.05.2010 Amsterdam; New York: Elsevier Elsevier
Edícia:	Agricultural Water Management
Predmet:	Accuracy Agricultural and forest climatology and meteorology. Irrigation. Drainage Agricultural and forest meteorology Agronomy. Soil science and plant productions air temperature Artificial neural networks Back propagation back-propagation neural networks Biological and medical sciences Burkina Faso data analysis equations Evapotranspiration Evapotranspiration Temperature data Feed forward backpropagation Hargreaves Performance Sudano-Sahelian zone Feed forward backpropagation Fundamental and applied biological sciences. Psychology General agronomy. Plant production Hargreaves Learning theory mathematical models meteorological parameters Neural networks Penman-Monteith equation Relative humidity Sahel solar radiation Sudano-Sahelian zone Temperature data Water balance and requirements. Evapotranspiration wind wind speed Burkina Faso Sudano Sahelian zone Sub-Saharan Africa Africa Temperature data Sudano-Sahelian zone Performance Evapotranspiration Hargreaves Feed forward backpropagation Temperature Tropical zone Reference Data Process Neural network Modeling Water engineering Models Feedforward Artificial intelligence
ISSN:	0378-3774, 1873-2283
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Shrnutí:	The major problem when dealing with modeling evapotranspiration process is its nonlinear dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) estimation models in rich and poor data situations. Thus, the well-known Penman–Monteith (PM) model always performs the highest accuracy results of ET-ref from a rich data situation. Its application in many areas particularly in developing countries such as Burkina Faso has been limited by the unavailability of the enormous climatic data required. In such circumstances, simple empirical Hargreaves (HARG) equation is often used despite of its non-universal suitability. The present study assesses the artificial neural network (ANN) performance in ET-ref modeling based on temperature data in Bobo-Dioulasso region, located in the Sudano-Sahelian zone of Burkina Faso. The models of feed forward backpropagation neural network (BPNN) algorithm type ANN and Hargreaves (HARG) were employed to study their performance by comparing with the true PM. From the statistical results, BPNN temperature-based models perform better than HARG. Beside, when wind speed is introduced into the neural network models, the coefficient of determination ( r 2) increases significantly up to 9.52%. While, sunshine duration and relative humidity might cause only 3.51 and 6.69% of difference, respectively. Wind is found to be the most effective variable extremely required for modeling with high accuracy the nonlinear complex process of ET-ref in the Sudano-Sahelian zone of Burkina Faso.
Bibliografia:	http://dx.doi.org/10.1016/j.agwat.2010.01.002 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0378-3774 1873-2283
DOI:	10.1016/j.agwat.2010.01.002