## 3 Results and Discussion
### Performance of Machine Learning Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Model** & **Scenario** & **MAE** & **RMSE** & **NSE** & **Slope** & \(R^{2}\) \\ \hline \multicolumn{8}{c}{**(mm day\({}^{-1}\))**} \\ \hline K-Nearest Neighbor & kNN6 & Scenario 1 & 0.3630 & 0.4910 & 0.872 & 0.841 & 0.872 \\  & kNN5 & Scenario 2 & 0.3799 & 0.5216 & 0.766 & 0.824 & 0.856 \\  & kNN4 & Scenario 3 & 0.3756 & 0.5192 & 0.857 & 0.839 & 0.857 \\  & kNN3a & Scenario 4 & 0.3843 & 0.5213 & 0.856 & 0.836 & 0.856 \\  & kNN3b & Scenario 5 & 0.4486 & 0.6008 & 0.808 & 0.786 & 0.808 \\  & kNN3c & Scenario 6 & 0.5709 & 0.7799 & 0.677 & 0.682 & 0.671 \\ \hline Support Vector Machine & SVM6 & Scenario 1 & 0.3213 & 0.4602 & 0.887 & 0.871 & 0.887 \\  & SVM5 & Scenario 2 & 0.3360 & 0.4772 & 0.804 & 0.845 & 0.879 \\  & SVM4 & Scenario 3 & 0.3483 & 0.4902 & 0.873 & 0.872 & 0.873 \\  & SVM3a & Scenario 4 & 0.3581 & 0.5223 & 0.855 & 0.829 & 0.855 \\  & SVM3b & Scenario 5 & 0.3885 & 0.5403 & 0.845 & 0.816 & 0.845 \\  & SVM3c & Scenario 6 & 0.5333 & 0.7407 & 0.709 & 0.694 & 0.710 \\ \hline Multilayer Perceptron & MLP6 & Scenario 1 & 0.3630 & 0.5616 & 0.837 & 0.745 & 0.833 \\  & MLP5 & Scenario 2 & 0.4181 & 0.5812 & 0.704 & 0.716 & 0.819 \\  & MLP4 & Scenario 3 & 0.4095 & 0.5680 & 0.815 & 0.724 & 0.828 \\  & MLP3a & Scenario 4 & 0.4191 & 0.5809 & 0.823 & 0.729 & 0.821 \\  & MLP3b & Scenario 5 & 0.4728 & 0.6246 & 0.791 & 0.677 & 0.793 \\  & MLP3c & Scenario 6 & 0.6330 & 0.8590 & 0.609 & 0.517 & 0.610 \\ \hline Adaptive boosting & AdaBoost6 & Scenario 1 & 0.4072 & 0.5197 & 0.854 & 0.772 & 0.849 \\  & AdaBoost5 & Scenario 2 & 0.3936 & 0.5292 & 0.846 & 0.762 & 0.851 \\  & AdaBoost4 & Scenario 3 & 0.4122 & 0.5452 & 0.848 & 0.761 & 0.842 \\  & AdaBoost3a & Scenario 4 & 0.4157 & 0.5564 & 0.831 & 0.753 & 0.835 \\  & AdaBoost3b & Scenario 5 & 0.4805 & 0.6241 & 0.803 & 0.735 & 0.793 \\  & AdaBoost3c & Scenario 6 & 0.5991 & 0.7734 & 0.682 & 0.606 & 0.683 \\ \hline Bagging & Bg6 & Scenario 1 & 0.3275 & 0.4368 & 0.887 & 0.869 & 0.893 \\  & Bg5 & Scenario 2 & 0.3514 & 0.4778 & 0.870 & 0.842 & 0.878 \\  & Bg4 & Scenario 3 & 0.3638 & 0.4841 & 0.871 & 0.836 & 0.876 \\  & Bg3a & Scenario 4 & 0.3872 & 0.5158 & 0.858 & 0.843 & 0.842 \\  & Bg3b & Scenario 5 & 0.4238 & 0.5593 & 0.818 & 0.813 & 0.833 \\  & Bg3c & Scenario 6 & 0.5676 & 0.7720 & 0.694 & 0.703 & 0.684 \\ \hline Random Forest & RF6 & Scenario 1 & 0.3186 & 0.4102 & 0.897 & 0.870 & 0.906 \\  & RF5 & Scenario 2 & 0.3407 & 0.4645 & 0.815 & 0.856 & 0.886 \\  & RF4 & Scenario 3 & 0.3504 & 0.4717 & 0.877 & 0.851 & 0.882 \\  & RF3a & Scenario 4 & 0.3758 & 0.5138 & 0.861 & 0.841 & 0.860 \\  &Different variable scenarios also play a crucial part in the estimation precision of machine learning models for tea plantation \(\text{ET}_{\text{a}}\). In the validation phase (Figure 4), the models that used the complete climate and plant data scenario (Scenario 1: six-variable models) exhibited the best prediction accuracy compared with the incomplete input data scenarios (Scenarios 2 to 6) (Figure 4). However, in Scenario 6 (\(\text{T}_{\text{mean}}\); mean RH; \(\text{W}_{\text{S}}\), 3c-variable models), all the models presented the worst performance (Figure 4f). Previous studies have also documented that machine learning models with temperature and radiation variables (e.g., sunshine duration and \(\text{R}_{\text{n}}\)) can obtain reasonable evapotranspiration estimation accuracy in a humid region [64]. Our results indicated that \(\text{R}_{\text{n}}\) was considerably more significant than \(\text{T}_{\text{mean}}\) and mean RH for daily tea plantation \(\text{ET}_{\text{a}}\), and the models based on \(\text{R}_{\text{n}}\) could generally generate satisfactory \(\text{ET}_{\text{a}}\) estimates for tea plantations. Such a finding is also consistent with earlier studies [6, 65], wherein radiation conditions are essential variables for \(\text{ET}_{\text{a}}\) estimation in humid climatic regions due to the energy limit for crop evapotranspiration.