## 4 Discussion
### Capability of Machine Learning Models and Their Comparison  
Our results demonstrated that the applied machine learning models with full input variables (\(T_{a}\), \(R_{n}\), \(R_{h}\), \(T_{s}\)) did an excellent job of explaining approximately average 92%, 94% and 78% of diurnal variances in GPP, \(R\) and NEE for the three stands, respectively (Table 5). These models also exhibited great capability in mapping the hourly variation of different carbon fluxes in response to environmental factors. According to the estimates of each method with different input combinations (Tables 4, 7 and 8), it is clear that the relative importance of driving variables is different with respect to each carbon flux. More specifically, the contributions of \(T_{a}\) and \(T_{s}\) in estimating daily GPP are similar and more than those of \(R_{n}\) and \(R_{h}\) at the three sites. \(T_{s}\) is the most predominant factor controlling the variability of \(R\) flux at each site, followed by \(T_{a}\) and then \(R_{n}\). However, considerable difference among the three forest sites was found in the contributions of these driving variables to NEE. At CA-Obs site, \(T_{a}\) and \(R_{n}\) provided similar contributions and played more important role than \(T_{s}\) and \(R_{h}\); At CA-Oas site, \(T_{s}\) offered the largest contribution to NEE, followed by \(T_{a}\) and then \(R_{n}\); The largest contribution to NEE at CA-Gro site was from \(R_{n}\), followed by \(T_{s}\) and then \(T_{a}\). Overall, the contribution of \(R_{h}\) to each carbon flux at the three sites was relatively lower, compared with the other three variables. In addition, these advanced machine learning models consistently performed better than the traditional MLR model for each carbon flux prediction. The predictive performance of these techniques at various terrestrial ecosystems was also confirmed by previous studies, mainly involving ANN and SVM approaches [12; 21; 47]. Additionally, it is worth mentioning here that both ANN and SVM machine learning techniques in recent years have been extensively accepted as preferred tools to upscale the terrestrial carbon fluxes from site to regional scale [13; 20; 48; 49]. More importantly, previous studies have pointed out that, based on the data derived from the remote sensing and EC techniques, data-driven models can perform better than land surface models for predicting the terrestrial carbon fluxes at regional and global scales [50; 51]. Furthermore, regarding all the examined machine learning models, our results revealed that these models can consistently estimate hourly and daily carbon fluxes at the three sites (Tables 5 and 9). Specifically, conventional ANN method remains adequately competent in reproducing the carbon fluxes due to its strong generalization ability. For this reason, recently, it has been continuously utilized in the estimation of carbon fluxes [22; 52; 53]. In addition, ANFIS, as a state of the art method, is designed by combining the strength of FIS and adaptive neural networks for the purpose of identifying the beneficial knowledge within the trained networks in the form of fuzzy logic expression. Therefore, ANFIS seems to be able to overcome the weakness of knowledge interpretation which is still a great challenge for neural networks. In theory at least, ANFIS could perform better than ANN method and this assumption has been proved by many previous studies in practical terms [54; 55]. However, in the present work, our results showed that ANFIS and ANN produced the similar estimates, which concurs with the results of both [PERSON] et al. [56] and [PERSON] et al. [57] for the hydrological time series forecasting.