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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Veröffentlicht in:	Agricultural water management Jg. 97; H. 5; S. 707 - 714
Hauptverfasser:	Traore, Seydou, Wang, Yu-Min, Kerh, Tienfuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.05.2010 Amsterdam; New York: Elsevier Elsevier
Schriftenreihe:	Agricultural Water Management
Schlagworte:	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
Online-Zugang:	Volltext
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