An accurate estimation of Evapotranspiration (ET) is an important issue in hydrology, water resources management and irrigation scheduling. There are a wide range of methods for estimation of ET, among which, machine learning techniques and remote sensing-based approaches demonstrated more reasonable results. Accordingly, this study attempts to compare the capability of two developed models of Gene-Expression Programming (GEP) and Surface Energy Balance Algorithm for Land (SEBAL) in estimation of ET, at four different climate types of Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C), and Arid-Warm (A-W). In this way, a-two year of daily records as weather variables (i.e., maximum and minimum temperature, dew-point temperature, vapor pressure, saturated vapor pressure, relative humidity, 24-h rainfall, sunshine hours, and wind speed) were considered as input variables, whereas ET values were computed as output variable (observed ET) by using FAO Penman–Monteith-56 method. After development of two predictive models, the statistical results were compared with well-known Hargreaves–Samani method. The results showed that while Hargreaves–Samani equation could not yield remarkable results in any of the climates, GEP and SEBAL demonstrated accurate predictions. In this way, GEP was the superior model in T-W (R² = 0.902 and RMSE = 0.713 mm/day) and A-W (R² = 0.951 and RMSE = 0.634 mm/day) climates but it dropped a bit in two other climates. However, SEBAL not only had the best performance in both climates of W-W (R² = 0.967 and RMSE = 0.515 mm/day) and A-C (R² = 0.990 and RMSE = 0.720 mm/day), but also demonstrated good predictions in T-W and A-W climates. Therefore, SEBAL is recommended as the best model for estimation of ET in all climate types.

准确估算蒸散量（ET）是水文学，水资源管理和灌溉计划中的重要问题。有多种估算ET的方法，其中，机器学习技术和基于遥感的方法展示了更合理的结果。因此，本研究试图比较四种不同气候类型的温带-暖（TW），湿的两种基因开发编程模型（GEP）和土地表面能平衡算法（SEBAL）在估计ET方面的能力。 -暖（WW），冷-冷（AC）和干-暖（AW）。通过这种方式，可以记录为期两年​​的每日天气变量（即最高和最低温度，露点温度，蒸气压，饱和蒸气压，相对湿度，24小时降雨，日照时间和风速）。被视为输入变量，而ET值则被计算为输出变量（观察到的ET），使用FAO Penman–Monteith-56方法。在开发了两种预测模型之后，将统计结果与著名的Hargreaves-Samani方法进行了比较。结果表明，尽管在任何一种气候下Hargreaves-Samani方程都无法获得显着的结果，但是GEP和SEBAL证明了准确的预测。这样，GEP在TW（R ²  = 0.902和RMSE = 0.713 mm /天）和AW（R ²  = 0.951和RMSE = 0.634 mm /天）气候中是优越的模型，但在其他两个气候中却有所下降。但是，SEBAL不仅在WW（R ²  = 0.967和RMSE = 0.515 mm / day）和AC（R ² = 0.990，RMSE = 0.720 mm /天），但在TW和AW气候中也显示出良好的预测。因此，建议将SEBAL作为估算所有气候类型中ET的最佳模型。