The Application of Artificial Neural Network Model in Estimation of Single Tree Volume Growth

This paper takes Pinus tabulaeformis plantation in Beijing mountainous areas for example, and establishes BP artificial neural network model which can be applied to the estimation of single tree volume growth. By using qualitative analysis and Pearson correlation analysis, the standard tree height (SH), standard diameter at breast height (SD), standard tree age (A) and storage per acre (CA) in the sample plots are selected as the input variables of the model. With the volume increment (Z) of standard trees in the last growing season in each sample plot as the output variable, the BP artificial neural network model of three-layer structure is established, and training and simulation towards the model have been carried out by using the measured comprehensive survey data of Pinus tabulaeformis plantation plot in Beijing mountainous areas. By applying error percentage method and linear regression method, the simulation effects of various models have been verified and compared. The results show that the simulation of artificial neural network model towards the single tree volume growth of Pinus tabulaeformis in Beijing mountainous areas is of high accuracy. This model can be applied to the effective prediction and simulation of tree growing process in this area after determining the reasonable input variables and network structures.