## 4 Discussion
### Effects of Different Inner Parameters on Their Corresponding Approaches  
It is further discovered that different internal parameters considerably affected the modeling ability of their corresponding methods for the carbon fluxes at all sites. In spite of the fact that our proposed methods presented a strong ability to reproduce carbon fluxes measured by the eddy covariance technique, their generalization performance was still restricted by the corresponding inner functions. To be specific, regarding the ANN method, it is well acknowledged that the Levenberg-Marquardt algorithm (trainlm) is the most widely utilized training function. However, according to our estimates, the ANN model with the Levenberg-Marquardt algorithm (ANN1) was not the universally optimal model for each carbon flux at all sites (Figures 1, 4 and 7). Therefore, it seems to be difficult to find a single common training function that guarantees the best calculation accuracy for all situations. Similarly, for the ELM method, a noteworthy point is that both the ELM1 (sine) and ELM2 (sigmoid) models were appreciably and consistently superior to the ELM3 (hard limit) model in all cases. Moreover, it was found that the ELM1 model did not always perform better than the ELM2 model in terms of the performance metrics for each flux at all sites. Consequently, the modeling capability of both ELM1 and ELM2, to some degree, depended on their given applications. For this reason, in order to accurately estimate the daily carbon fluxes, both the sine and sigmoid algorithms should be used as alternative transfer functions. In addition, it was observed that the SVM1 model (RBF kernel) substantially surpassed the SVM2 (polynomial kernel) and SVM3 (sigmoid kernel) models in terms of mapping the complex carbon exchange processes at the five sites, which concurs with the previous findings for the applications of other fields [73; 74]. In addition, in most cases, the SVM2 and SVM3 models offered the worst modeling accuracy in comparison with the other three methods (ANN, ELM, and ANFIS) with different internal parameters. Therefore, when dealing with a nonlinear regression problem, it is particularly important to determine a proper kernel function when a SVM model is developed. Lastly, the potential and effectiveness of the ANFIS technique was explored to estimate the daily carbon fluxes. Additionally, investigating the potential impacts of the inner parameters on the predictive performance of the ANFIS was also taken into consideration as a second important task for this method. Specifically, we not only examined the effects of different types of membership functions under a grid partitioning algorithm but also investigated the influences of different types of FISs. Regarding the former, it was demonstrated that appreciable differences in the forecasting ability of the ANFIS models existed among the eight various membership functions for each carbon flux at all sites. The prediction accuracy of these ANFIS models depended on the membership functions. No single membership function was universally optimal for the grid partitioning algorithm for this study. Based on this finding, the trial and error method was recommended in order to determine the most appropriate membership function. On the other hand, based on the comparison of three different identification methods for generating FISs, most of the ANFIS models with both the subtractive clustering and fuzzy c-means algorithms provided similar modeling precision for each flux at all sites and performed substantially better than the ANFIS models with the grid partitioning algorithm (ANFIS1-8). Therefore, both the subtractive clustering and fuzzy c-means algorithms should be viewed as the preferable alternative methods for developing an optimal ANFIS model for predicting daily carbon fluxes. Given the findings above, our research has confirmed the importance of evaluating the effects of inner parameters on their respective methods in order to more accurately characterize the terrestrial carbon fluxes.