Evaporation is one of the most important processes in meteorology and hydrology. It is necessary to develop approaches to estimate the evaporation rates. The main purpose of this investigation was to evaluate the wavelet transform models in estimation of evaporation in three different climates of Iran. The proposed wavelet- artificial neural network (WANN) and artificial neural network (ANN) models were developed to simulate daily evaporation data of Ahwaz Station with arid climate, Sari Station with per-humid climate and Hamedan Station with semi-arid climate. Temperature, relative humidity, and wind speed parameters were used as input. The results showed that in both arid and semi-arid climate, the WANN model was performed better than that the ANN did. The coefficients of determination (r) were 0.91 and 0.82 for Ahwaz and Hamedan and Normalized root mean square errors (NRMSE) for these stations were 0.26% and 0.27%, respectively. But in Sari Station with per-humid climate, the accuracy of ANN was more than that with WANN. The coefficient of determination and NRMSE values in ANN were 0.78 and 0.44 %, respectively, in contrast, the r and NRMSE values for WANN were 0.74 and 0.47%. Therefore, it can be concluded that evaporation can be successfully estimated using ANN and WANN models in different climates.
