  Wood increment is critical information in forestry management. Previous
studies used mathematics models to describe complex growing pattern of forest
stand, in order to determine the dynamic status of growing forest stand in
multiple conditions. In our research, we aimed at studying non-linear
relationships to establish precise and robust Artificial Neural Networks (ANN)
models to predict the precise values of tree height and forest stock volume
based on data of Chinese fir. Results show that Multilayer Feedforward Neural
Networks with 4 nodes (MLFN-4) can predict the tree height with the lowest RMS
error (1.77); Multilayer Feedforward Neural Networks with 7 nodes (MLFN-7) can
predict the forest stock volume with the lowest RMS error (4.95). The training
and testing process have proved that our models are precise and robust.
