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