# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 1 Introduction
With the recent advances in machine learning, many state-of-the-art data-driven techniques have been presented, mainly including group method of data handling (GMDH), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM). The ability of these methods to perform with limited climatic inputs in terms of elucidating the complex processes of water vapor fluxes between land surface and the atmosphere, such as reference evapotranspiration (ET) and evaporation, has been adequately demonstrated by previous studies [31; 32; 33]. Moreover, these methods are also widely used for regression analysis in many other fields, such as modeling of hydrological time series (e.g., stream-flow and rainfall) [34; 35; 36], forecasting of renewable energy (e.g., solar radiation and wind power) [37; 38], and modeling of meteorological time series (e.g., air temperature and precipitation) [39; 40]. In contrast, comparatively few studies relevant to these methods have been carried out to map the actual ET and carbon fluxes, here including gross primary production (GPP), ecosystem respiration (\(R\)) and net ecosystem exchange (NEE), in terrestrial ecosystems based on the data measured at the flux tower sites by the eddy covariance technique. Despite the considerable technological efforts involved in addressing the aforementioned issues of carbon and water flux exchanges, however, relatively little work has been conducted to compare the ability of these traditional techniques (ANN and SVM). Moreover, with the increasing number of novel invented methods, the need for such a comprehensive comparison of conventional with advanced methods should to be more crucial. Namely, it can be considered as a helpful benchmark for ensuringthe estimated accuracy of solving different issues by properly selecting an effective method from a variety of machine learning techniques. Therefore, the comparison of these methods, including ANN, SVM, GRNN, GMDH, ELM and ANFIS was carefully considered in this study. Our major goal is to utilize six different data-driven techniques based on machine learning to estimate the carbon and water fluxes with continuous six-year observation data from a flux tower site in a forest ecosystem. The specific aims are threefold as follows: (1) to investigate the feasibility and capability of various data-driven models, including GRNN, GMDH, ELM and ANFIS, for simulating the daily carbon and water fluxes at the ecosystem level; (2) to demonstrate that these newly proposed modern models can be used as desirable complements to the traditionally accepted ANN and SVM models; and (3) to examine the modeling differences between the ET and three primary components of carbon fluxes (GPP, \(R\) and NEE). In addition, this study focuses on examining the modeling ability of aforementioned models only at a single flux tower site. It should be pointed out that these methods can also be used to upscale the carbon and water fluxes from site to regional scale with remote sensing data. However, it is beyond the scope of the present work and will be carried out in our follow-up investigation.