Daily reference evapotranspiration (ET0), as a dependent variable, was estimated for two weather stations in South Korea, using 8 years (1985–1992) of measurements of independent variables of air temperature, sunshine hours, wind speed and relative humidity. The model uses the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for estimating daily ET0. In the first part of the study, the applied models were trained, tested and validated using various combinations of the recorded independent variables, which corresponded to the Hargreaves–Samani, Priestly–Taylor and FAO56-PM equations. The goodness of fit for the models was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe coefficient (NS). In the second part of the study, the estimated solar radiation data were applied as input parameters (for the same input combinations, as the first part), instead of recorded sunshine values. The results indicated that the both applied ANFIS and ANN models performed quite well in ET processes from the available climatic data. The results also showed that the application of estimated solar radiation data instead of the recorded sunshine values decreases the models’ accuracy.
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Research Article|
July 30 2012
Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)
Ana Pour-Ali Baba;
Ana Pour-Ali Baba
1Department of Agronomy, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran
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Jalal Shiri;
2Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
E-mail: j_shiri2005@yahoo.com
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Ozgur Kisi;
Ozgur Kisi
3Civil Engineering Department, Architectural and Engineering Faculty, Canik Basari University, Samsun, Turkey
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Ahmad Fakheri Fard;
Ahmad Fakheri Fard
2Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
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Sungwon Kim;
Sungwon Kim
4Department of Railroad and Civil Engineering, Dongyang University, Yeongju, Republic of Korea
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Rouhallah Amini
Rouhallah Amini
5Agronomy Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
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Hydrology Research (2013) 44 (1): 131–146.
Article history
Received:
May 03 2011
Accepted:
November 30 2011
Citation
Ana Pour-Ali Baba, Jalal Shiri, Ozgur Kisi, Ahmad Fakheri Fard, Sungwon Kim, Rouhallah Amini; Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research 1 February 2013; 44 (1): 131–146. doi: https://doi.org/10.2166/nh.2012.074
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