Abstract
This study compares the daily potato crop evapotranspiration (ETC) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ETC was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ETC at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R 2 > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.
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Abbreviations
- ETo :
-
Reference evapotranspiration
- ETC :
-
Crop evapotranspiration
- ANN:
-
Artificial neural network
- NNGA:
-
Neural network–genetic algorithm
- MNLR:
-
Multivariate nonlinear regression
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Acknowledgments
The authors wish to thank the Islamic Republic of Iran Meteorological Office (IRIMO) for providing the requisite meteorological data. Special thanks are due to the different people who collected the weather data during 2000–2005 from Tabriz weather station.
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Aghajanloo, MB., Sabziparvar, AA. & Hosseinzadeh Talaee, P. Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput & Applic 23, 1387–1393 (2013). https://doi.org/10.1007/s00521-012-1087-y
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DOI: https://doi.org/10.1007/s00521-012-1087-y