Evapotranspiration (ET) is a major hydrologic process to assess water budgets in terrestrial ecosystems. Since measurement of ET may involve labor intensive field technics in large areas, estimation is preferred in most cases. The FAO Penman-Monteith (PM FAO-56) is a widely recognized reference evapotranspiration (ET_o) method for potential evapotranspiration calculations. The method requires a detailed and comprehensive meteorological data set; however, some empirical methods and models have attempted to calculate ET with less data. In this study, Makkink (ET_Mak), Hargreaves–Samani (ET_Har), Thornthwaite (ET_Thor), Blaney–Criddle (ET_BC), and Penman (ET_PM) were tested against the PM FAO-56. Penman method has achieved the highest accuracy among the empirical methods. In addition, the potential of artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and multiple linear regression (MLR) for estimating ET_o were investigated in a semi-arid Central Anatolian Region of Turkey. The results obtained with the ANN (based on multi-layer perceptron) and SVM models performed better than other tested data-driven models and empirical methods. These models could be used most effectively at elevation range of 850–1000 m. According to our results MLP, SVM, and Penman methods provided good performances in semi-arid regions in agricultural planning and water resources management studies. Furthermore, we concluded that integrating maximum temperature may result in improved accuracy in ET model simulations in semi-arid regions.

蒸散 (ET) 是评估陆地生态系统水收支的主要水文过程。由于 ET 的测量可能涉及大面积的劳动密集型现场技术，因此在大多数情况下首选估算。FAO Penman-Monteith (PM FAO-56) 是一个广泛认可的参考蒸发量 (ET _o) 计算潜在蒸散量的方法。该方法需要详细而全面的气象数据集；然而，一些经验方法和模型试图用较少的数据计算 ET。在这项研究中，针对 PM FAO-56 对 Makkink (ET_Mak)、Hargreaves-Samani (ET_Har)、Thornthwaite (ET_Thor)、Blaney-Criddle (ET_BC) 和 Penman (ET_PM) 进行了测试。Penman 方法在经验方法中取得了最高的准确率。此外，人工神经网络 (ANN)、支持向量机 (SVM)、随机森林 (RF) 和多元线性回归 (MLR) 用于估计 ET _o的潜力在土耳其半干旱的安纳托利亚中部地区进行了调查。使用 ANN（基于多层感知器）和 SVM 模型获得的结果比其他经过测试的数据驱动模型和经验方法表现更好。这些模型可以在 850-1000 m 的海拔范围内最有效地使用。根据我们的结果，MLP、SVM 和 Penman 方法在半干旱地区的农业规划和水资源管理研究中提供了良好的性能。此外，我们得出结论，整合最高温度可能会提高半干旱地区 ET 模型模拟的准确性。