# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 5 Conclusions  
With the advance of machine learning techniques, many modern data-driven approaches have been developed during the past few decades, which leads to the present predicament of what modeling technique should be chosen in a practical application, predominantly due to the lack of comprehensive benchmark studies. In order to conquer this predicament, a comparative research plays an essential role in obtaining a full-scale overview of different data-driven methods, specifically aiming at gaining deep insights into their strengths and limitations and drawing some helpful conclusions with regard to their predictability and robustness. In this context, the main basis of the current work was to investigate the suitability of our newly proposed models, including GRNN, GMDH, ELM and ANFIS, in mapping the non-linear relationships that dominate the exchanges of the forest carbon and water fluxes at a flux tower site. In addition, these models were compared and evaluated for the first time with the classical ANN and SVM models in terms of several performance indices (\(R^{2}\), NSE, RMSE and MAE). It was found the ANN model provided the best estimates for GPP and NEE, whereas the ANFIS model achieved the best for \(R\), indicating that no single model was consistently superior to others for the carbon flux prediction. Therefore, the use of a variety of methods is of particular importance for obtaining adequately accurate estimates for the carbon fluxes. In contrast, for all the carbon fluxes and ET estimation, the GMDH model consistently produced somewhat worse results, and accordingly may be not recommended for the present applications. Moreover, there were considerable differences among all the carbon and water fluxes. When taken as a whole, all the models generated the similar satisfactory predictive accuracy for GPP, \(R\) and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. The present investigation has manifested the feasibility and validity of several novel data-driven techniques for forecasting the carbon and water fluxes measured by the eddy covariance technique. These models can be used as attractive complements to traditional ANN and SVM models, except for GMDH, which may be due to involvement of large numbers of high-dimensional matrix calculations. In addition, these modern techniques provide many new alternative approaches for data collectors and processors to interpolate the missing data during the long-term eddy covariance measurements, which is of importance for guaranteeing the estimated accuracy in the further studies, when using these related measurements for the parameterization of process-based models and the validation of their estimates for forecasting carbon and water fluxes, and the assessment of published carbon and flux products from remote sensing techniques. More importantly, it is expected that these powerful methods offer novel perspective for up-scaling the carbon and water fluxes from ecosystem to regional or global scale with remote sensing data, as our follow-up investigation, which is particularly essential for the scientific community to intentionally ascertain the carbon and water budgets and further provide helpful information for policy makers responding to present and future climate change. The work was financially supported by the Natural Science Fund of China (No. 41672324, No. 41430317, and No. 41402291), the National Science and Technology Major Projects (No. 2016 ZX05044-002), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. The present research used the eddy covariance data acquired and shared by the Fluxnet-Canada Research Network. The authors acknowledge the project investigator who contributed to the eddy covariance flux measurements for providing valuable data for this study. The authors also gratefully acknowledge three anonymous reviewers for their constructive comments.