Application of Multilayer Feedforward Neural Networks in Predicting Tree Height and Forest Stock Volume of Chinese Fir

TL;DR

This study develops neural network models to accurately predict Chinese fir tree height and forest stock volume, demonstrating their robustness and precision in forestry management applications.

Contribution

The paper introduces specific multilayer feedforward neural network models tailored for predicting tree height and forest volume in Chinese fir, improving prediction accuracy over traditional methods.

Findings

MLFN-4 predicts tree height with RMS error 1.77

MLFN-7 predicts forest volume with RMS error 4.95

Models are shown to be precise and robust

Abstract

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.