Applying an artificial neural network to simulate and predict Chinese fir (Cunninghamia lanceolata) plantation carbon flux in subtropical China

Carbon (C) flux between forest ecosystems and the atmosphere is an important ecosystem C cycling component. Modeling C flux plays a critical role in assessing both C cycles and budgets. This study aimed to determine important non-redundant input variables to quantify C flux and to develop a new application of a genetic neural network (GNN) model that accurately simulates C flux. Four input variables (atmospheric CO2 concentration, air temperature, photosynthetically active radiation (PAR), and relative humidity) were fixed, whereas three additional input variables (wind speed, soil temperature, and rainfall) were randomly combined to compile eight combinations of input variables (CIV 1–CIV 8). C flux and meteorological data were collected over a four-year period between January 2008 and December 2011 at the Huitong National Research Station of Forest Ecosystem. Results showed that CIV 8 (grouping atmospheric CO2 concentration, air temperature, PAR, relative humidity, wind speed, and soil temperature) performed best, yielding a correlation coefficient (R2) of 0.87, outlier of 0.79%, and a root mean squares of errors (RMSE) of 0.11. C flux data during summer generally provided the best performance with R2 ranging from 0.74 to 0.82, volumetric fitting (Ivf) ranging from 1.00 to 1.02, and outliers ranging from 1.20% to 1.40%. Spring data performance ranked second and winter last. When combining seasonal data to reflect the entire year, R2 ranged from 0.77 to 0.83, Ivf ranged from 0.92 to 0.97, outliers ranged from 1.40% to 1.78%, and RMSE ranged from 0.10 to 0.11, indicating that the GNN model is capable in capturing C flux dynamics while successfully simulating and predicting C flux in a Cunninghamia lanceolata plantation in subtropical China.