## 3 Results
### Evaluation of Model Performance for NEE flux in Different Ecosystems  
Figure 7a presents the overall rankings of model efficiency in the prediction of daily NEE for five different sites and four different machine learning methods with their respective internal functions in the testing period. As illustrated in the figure, the ANN1, ANN2, ANN9, ANFIS2, and SVM1  
Figure 6: Daily \(R\) observed via the eddy covariance method and modeled by the optimal machine learning models for the five sites in the whole period including training (the first four years), validation (the fifth year), and testing (the final year) periods. **(a)** ANN2 at AT-Neu; **(b)** ANN8 at SE-Deg; **(c)** ELM1 at US-Bo1; **(d)** ANN5 at CA-Oas; **(e)** ANN4 at CA-Obs. models at CA-Obs obtain higher model efficiency, while the ANFIS4, ANFIS6, and SVM3 models at AT-Neu, and the ANFIS7 and SVM3 models at US-Bo1 achieve lower model efficiency. Moreover, the applied models generally achieve similar performance at both CA-Obs and SE-Deg, and the overall performances at these two sites are superior to those at the other three sites. The worst model performance occurs at AT-Neu. The model efficiency rankings of the applied methods with their respective internal functions at each site in predicting NEE in the testing period are shown in Figure 7b. At AT-Neu, among the 26 models, the ANN1, ANN2, ANN9 and ANFIS10 models obtain higher model efficiency than those at the other models. Similarly, for the other four stands, the first four highest efficiency models can be generalized as follows: ANN1, ANN2, ANN3, and ANN7 at SE-Deg; ANN1, ANN3, ANN5, and SVM1 at US-Bo1; ANN1, ANFIS1, ANFIS9, and ANFIS10 at CA-Oas; ANN1, ANN2, ANN9, and SVM1 at CA-Obs. Furthermore, it should be particularly emphasized that the ANN1 model attains the highest  
Figure 7: Model performance rankings of net ecosystem exchange (NEE) estimation in the testing period: (**a**) for five different sites and four different machine learning methods with their respective internal functions (130 models); (**b**) for four different machine learning methods with their respective internal functions (26 models) at each site. model efficiency at AT-Neu and US-Bo1, the second highest at CA-Obs, and the third highest at SE-Deg and CA-Oas. It can be clearly seen in Figure 7b that the selection of internal parameters is of importance for the performance of the used models in forecasting daily NEE in the five sites. The best models for each data-driven approach are briefly generalized as follows: for the ANN, the ANN1 model gains the best model efficiency for AT-Neu and US-Bo1, the second best for CA-Obs and the third best for both SE-Deg and CA-Oas; for the ELM, except for SE-Deg, ELM1 and ELM2 generally yield similar model performance, and these two models outperform the ELM3 model at the other four sites; for the ANFIS, except for SE-Deg, the ANFIS9 outperforms the other ANFIS models at the other four sites; for the SVM, SVM1 appreciably surpasses the SVM2 and SVM3 models at all sites, and the SVM2 and SVM3 models have similar model performance in all five stands. Table 5 exhibits the estimated precision of the employed approaches with the optimal internal functions in the prediction of daily NEE in all stands. It can be clearly seen in the table that the ANN1 model with higher \(R^{2}\) and IA values and lower RMSE and MAE values in the testing period is superior to the other optimal models at AT-Neu, which is similar to the ANN3, ANN1, ANFIS10 and ANN2 models at SE-Deg, US-Bo1, CA-Oas, and CA-Obs, respectively. This is confirmed by Figure 7b. In addition, as displayed in Table 5, all optimal models obtain similar accuracy at both CA-Oas and CA-Obs sites and these models achieve higher accuracy than those of the other models at AT-Neu, SE-Deg, and US-Bo1 sites, with respect to \(R^{2}\), IA, RMSE, and MAE statistics. The observed and estimated daily NEE by the methods with the best internal functions at the five sites are illustrated in Figures 8 and 9.