## 3 Results and Discussion
### Performance of Machine Learning Models  
Table 4 provides the performance of three EL machine learning models (RF, bagging, and Ad) and the three common machine learning models (KNN, SVM, and MLP) in predicting daily tea plantation ET\({}_{\rm a}\) during the testing phase under six input scenarios. The statistical results indicated that the RF model had higher \(R^{2}\) values (0.84-0.91) and lower RMSE (0.41-0.56 mm day\({}^{-1}\)) during the testing phase, followed by bagging, SVM, KNN, and Ad, except in Scenario 6, whereas MLP exhibited the lowest performance among the 36 estimation models in predicting tea plantation ET\({}_{\rm a}\) (Table 4). In addition, the experimental model of evapotranspiration (the \"GG\" model) has good simulation results for tea plantation evapotranspiration (\(R^{2}\): 0.87; RMSE: 0.49 mm day\({}^{-1}\)). However, the experimental model needed more driving data (for example, the soil heat flux and drying force) than that required for the machine learning models, which are hard to obtain from conventional meteorological stations. Moreover, there are different performances for the estimation of tea plantations' evapotranspiration between the six datasets' scenarios. In Scenario 1, all the models achieved high-performance accuracy with high values of \(R^{2}\) (0.833-0.906) and low RMSE (0.4102-0.5616 mm day\({}^{-1}\)). The datasets for Scenarios 2, 3, 4, and 5 provided the second, third, fourth, and fifth performance accuracy, respectively. However, for Scenario 6, performance accuracy dropped dramatically in all the models, with high RMSE and MAE but low \(R^{2}\). ET\({}_{\rm a}\) values in the validation phase illustrate that the machine learning models exhibit varying prediction accuracy under different input scenarios (Figure 4). A similar pattern was observed in the testing phase. RF also achieved the best performance in the validation phase for assessing the daily ET\({}_{\rm a}\) of tea plantations  
Figure 3: Average monthly distributions of R\({}_{\rm n}\) (**a**), T\({}_{\rm mean}\) (**b**), RH, W\({}_{\rm s}\) (**c**), and ET\({}_{\rm a}\) (**d**) of the study area. by using different input combination strategies, followed by bagging and SVM. In general, EL models (RF and bagging) were superior to the other models (KNN, MLP, Ad, and SVM). The authors of [48] revealed that RF outperformed other models in evaluating the ET\({}_{\text{a}}\) of humid subtropical wetlands. In [18], it was found that tree-based EL models obtained more precise estimation values of evapotranspiration than common machine learning models (e.g., SVM). Meanwhile, slight differences were observed between the bagging and RF models in estimating tea plantations' ET\({}_{\text{a}}\) (MAE < 3% and RMSE < 2.8%) for the six input scenarios (Table 4). The results also supported our hypothesis that bagging achieved high prediction accuracy in daily ET\({}_{\text{a}}\) because its algorithm principles were similar to those of RF. By contrast, the performance of the Ad model did not meet its expectations. Overall, the RF, bagging, and SVM models were the best models for estimating the daily ET\({}_{\text{a}}\) of tea plantations on the basis of the aforementioned performance comparison criteria.