Estimating daily pan evaporation using artificial neural network in a semi-arid environment

Abstract

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (EPan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate EPan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R2 > 0.88 and RMSE < 1.2 mm day−1), but the ANN method gave better estimates than the calibrated Hargreaves method.