Evaporation plays a key role in water resources management in arid and semiarid climatic regions. This is the first time that an artificial neural network (ANN) model is applied to estimate evaporation in a hot and dry region (BWh climate by the Köppen classification). It has been found that ANN works very well at the study site and, further, an integrated ANN and autoregressive with exogeneous inputs can have an improved performance over the traditional ANN. Both models significantly outperformed the two empirical methods. It has been demonstrated that the important weather factors to be included in the model inputs are wind speed, saturation vapor pressure deficit, and relative humidity. This result is different from all those reported in the literature and is interestingly linked with a 1936 study by Anderson, who emphasized the importance of saturation vapor pressure deficit. As evaporation is a nonlinear dynamic process, the selection of suitable input weather variables has been a complicated and time-consuming task for modelers. In this study, a new data analysis tool called the gamma test has been used to identify the best combination of model inputs prior to model construction and evaluation.