Data/hora: 06/06/2024 07:58:33

Provedor de dados: 	111	
País: 	China	
Título: 	Utilization of new computational intelligence methods to estimate daily Evapotranspiration of wheat using Gamma pre processing	
Autores: 	mohammadigolafshani, nima; koulaian, ali.	
Data: 	2018-12-26	
Ano: 	2018	
Palavras-chave: 	Adaptive neuro-fuzzy inference system; Adaptive neuro-fuzzy inference-wavelet; Evapotranspiration; Neural network; Wheat.	
Resumo: 	Estimation of evapotranspiration (ET) is needed in water resources management, scheduling of farm irrigation, and environmental assessment. Hence, in practical hydrology, it is often crucial to reliably and constantly estimate evapotranspiration. Accordingly, 3 artificial intelligence (AI) techniques comprising adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference- wavelet (ANFIS-Wavelet) were applied in to estimate wheat crop evapotranspiration (ETc). A case study in a Dashtenaz region located in Mazandaran, Iran, was conducted with weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation since 2003 to 2011. The daily climatic data from Dashtenaz stations, (8 stations), were used as inputs AI models for estimating ET0.The assessments of the AI models were compared with the wheat crop evapotranspiration (ETc) values measured by crop coefficient approach and standard FAO-56 Penman–Monteith equation. Similarly, determination coefficient (R2), Nash–Sutcliffe (CNS) efficiency coefficient model and root mean squared error (RMSE) were applied to compare the models performance and to decide on the best one. The outcomes attained with the ANFIS-Wavelet model (with trapezoidal member function’s combination with Mayer wavelet) were better than ANN and ANFIS models for ETc estimation and confirmed the potential of this technique to provide useful tool in ETc modeling.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/4459	
Editor: 	International Commission of Agricultural and Biosystems Engineering	
Relação: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/4459/2571

http://www.cigrjournal.org/index.php/Ejounral/article/view/4459/2779

http://www.cigrjournal.org/index.php/Ejounral/article/downloadSuppFile/4459/1816	
Formato: 	text/plain

application/pdf	
Fonte: 	Agricultural Engineering International: CIGR Journal; Vol 20, No 3 (2018): CIGR Journal; 1-12

1682-1130	
Fonte: 	Copyright (c) 2017 Agricultural Engineering International: CIGR Journal