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

被引：0

作者：

Huang, Xiaohui ^{[1
]}

Hu, Xing ^{[1
]}

Jiang, Weichang ^{[2
]}

Yang, Zhi ^{[1
]}

Li, Hao ^{[3
]}

机构：

[1] Sichuan Univ, Coll Software Engn, Chengdu 610064, Peoples R China

[2] Sichuan Univ, Coll Comp Sci, Chengdu 610064, Peoples R China

[3] Sichuan Univ, Coll Chem, Chengdu 610064, Peoples R China

来源：

2014 IEEE WORKSHOP ON ELECTRONICS, COMPUTER AND APPLICATIONS | 2014年

关键词：

Artificial neural networks; Multilayer Feedforward Neural Networks; Chinese fir; tree height; forest stock volume;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：610 / 613

页数：4