Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

Abstract

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET0) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET0 and that the ANNs with an R2 of 0.95 and a root mean square error (RMSE) of 0.41 mm day−1 simulated ET0 better than the Hargreaves equation, which had an R2 of 0.91 and a RMSE of 0.51 mm day−1.