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The aim of this study is estimation of monthly pan evaporation using artificial neural networks and support vector machines. In the current study, the meteorological variables including air temperature, solar radiation, wind speed, relative humidity and precipitation were considered monthly. The R" of ANNs and SVMs models were obtained 0.940 and 0.936, respectively; whereas the Mean Square Error values (MSE) were 1265.22 and 40.98, respectively. Both ANNs and SVMs approaches work well for the data set used in this region, but the SVMs technique works better than the ANNs model.