Water resources cannot be effectively managed unless potential evapotranspiration is determined with high accuracy at headwater catchments. The study presents the most suitable feature combinations for building a reliable potential evapotranspiration (PET) model in the headwater catchments of Ogun River Basin, Southwest Nigeria. Using rainfall (R), wind speed (U₂), sunshine hour (S), relative humidity (Rh), minimum temperature (Tmin) and maximum temperature (Tmax) as input features, a Random Forest (RF) model was developed to predict PET. Although the model yielded satisfactory results, it was subjected to the minimal depth and percentage increase in mean square error (%IncMSE). This was done to reduce the input features and to increase model accuracy. Thereafter various combinations of important input features were examined in order to establish the best combinations required to yield optimum results. The study revealed that although Tmax (%IncMSE of 652.09, p value < 0.05) and Rh (%IncMSE of 254.36, p value < 0.05) were the most important predictors of PET, a more reliable RF model was achieved when S and U₂ were combined with them. Consequently, this study presents RF with a combination of four parameters (Tmax, Rh, S and U₂) as an excellent computational technique for the prediction of PET in headwater catchments.

除非在源头集水区以高精度确定潜在的蒸散量，否则无法有效管理水资源。该研究提出了在尼日利亚西南部奥贡河流域源头集水区建立可靠的潜在蒸散量 (PET) 模型的最合适的特征组合。利用降雨量 ( R )、风速 ( U ₂ )、日照时数 ( S )、相对湿度 (Rh)、最低温度 (Tmin) 和最高温度 ( Tmax) 作为输入特征，开发了随机森林 (RF) 模型来预测 PET。尽管该模型产生了令人满意的结果，但它受到了最小深度和均方误差 (%IncMSE) 增加百分比的影响。这样做是为了减少输入特征并提高模型精度。此后，检查了重要输入特征的各种组合，以建立产生最佳结果所需的最佳组合。研究表明，虽然T max（%IncMSE 为 652.09，p值 < 0.05）和 Rh（%IncMSE 为 254.36，p值 < 0.05）是 PET 最重要的预测因子，但当S和U ₂与他们结合在一起。因此，本研究将 RF 与四个参数（T max、Rh、S和U ₂）的组合作为一种用于预测源头集水区 PET 的出色计算技术。