In arid areas, the estimation of evaporation rates plays a considerable role on both water resources management and agricultural activities. Hence, it is of utmost importance to determine the best model to predict these rates. This study investigates the applicability of using quantile regression forest in predicting the pan evaporation. The model was configured using data from three different meteorological stations located in arid to semi-arid climates in Iraq. These stations were in the cities of Baghdad, Basrah, and Mosul, which are located in the middle, south, and north of the country, respectively. The performance of quantile regression forests was compared with three kinds of artificial intelligence methods i.e. random forests, support vector machine, and artificial neural network in addition to the conventional multiple linear regression models. The maximum temperature (°C), minimum temperature (°C), relative humidity (%), and wind speed (m/sec) were used as input parameters to the predictive models. The collected data (from 1990 to 2013) was randomly partitioned into two periods; 75% for calibration and 25% for validation. The fivefold cross validation was used during the calibration stage for better model predictability. The results were evaluated using three performance criteria: determination coefficient (R²), root mean square error (RMSE), and Nash and Sutcliff coefficient efficiency (NSE). Results showed that the quantile regression forests model attained the optimum performance among the evaluated methods. The value of R², RMSE, and NSE during validation was 0.99, 17.96 mm, and 0.99 at Baghdad; 0.98, 23.36 mm, and 0.98 at Basrah; and 0.99, 14.44 mm, and 0.99 at Mosul, respectively. Therefore, this method is the most appropriate one to use for predicting evaporation rates in arid to semi-arid climates.

在干旱地区，蒸发率的估算对水资源管理和农业活动都起着重要作用。因此，确定预测这些速率的最佳模型至关重要。这项研究调查了使用分位数回归林预测锅蒸发的适用性。该模型是使用来自伊拉克干旱至半干旱气候的三个不同气象站的数据进行配置的。这些站点分别位于该国中部，南部和北部的巴格达，巴士拉和摩苏尔。将分位数回归森林的性能与三种人工智能方法（即随机森林，支持向量机，以及传统的多元线性回归模型之外的人工神经网络。最高温度（°C），最低温度（°C），相对湿度（％）和风速（m / sec）被用作预测模型的输入参数。收集的数据（1990年至2013年）随机分为两个时期：校准为75％，验证为25％。在校准阶段使用了五重交叉验证，以实现更好的模型可预测性。使用三个性能标准对结果进行了评估：确定系数（75％用于校准，25％用于验证。在校准阶段使用了五重交叉验证，以实现更好的模型可预测性。使用三个性能标准对结果进行了评估：确定系数（校准为75％，验证为25％。在校准阶段使用了五重交叉验证，以实现更好的模型可预测性。使用三个性能标准对结果进行了评估：确定系数（R ²），均方根误差（RMSE）以及纳什和苏克利夫系数效率（NSE）。结果表明，在评估方法中，分位数回归森林模型获得了最佳性能。在验证期间，R ²，RMSE和NSE的值分别为0.99、17.96毫米和0.99，在巴格达；巴士拉（Basrah）为0.98、23.36毫米和0.98；在摩苏尔分别为0.99、14.44毫米和0.99。因此，该方法是预测干旱至半干旱气候下蒸发速率的最合适方法。