The reference evapotranspiration (ET₀) estimates is important for water resources and irrigation management. The Penman-Monteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwise multiple linear regression method to estimate daily ET₀ with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET₀, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors.

参考蒸散量（ET ₀）估计值对水资源和灌溉管理非常重要。Penman-Monteith方程以其精确度而闻名，但需要大量并非总是可用的气候参数。因此，本研究旨在评估机器学习技术（立方回归，具有贝叶斯正则化的人工神经网络，具有线性核函数的支持向量机）和逐步多元线性回归方法以评估每日ET _{0的性能。}巴西农业边境（MATOPIBA）的天气数据有限。使用了从23个气象站获得的2000年至2016年的气候数据。评价了五个数据方案：（i）所有变量，（ii）辐射和温度，（iii）温度和相对湿度，（iv）风速和温度，以及（v）温度。结果表明，即使没有一些变量，机器学习方法在估计ET ₀方面也很鲁棒。在仅使用温度数据评估的方法中，立体回归显示出更好的性能。仅使用温度估算大豆和玉米作物的需水量时，立体回归和经过校准的Hargreaves-Samani方程显示出最小的误差。