Height-diameter Model of Cunninghamia lanceolata Based on Deep Neural Network
By using the deep Neural Network(DNN)model to establish the tree height-diameter model of Cunninghamia lanceolata,we are seeking a more efficient method for predicting the tree height of Cunninghamia lanceolata.The diameter at breast height and tree height data of Cunninghamia lanceolata in 49 plots of state-owned forest farm in Qingzhen City,Guizhou Province were studied,and divided into different proportions of training set and test set data.This consists of training set data(20％,30％,40％,50％,60％,70％,80％,respectively)and test set data(80％,70％,60％,50％,40％,30％,20％,respectively).DNN was used to build a tree height-diameter model,and the model was compared with 11 traditional basic models.Select the model with the best predictive performance by comparing the results of R2,RMSE and MAE.Adding the ratio of diameter at breast height to average diameter at breast height(DDH)of dominant trees based on the optimal model to improve the prediction accuracy.When the training set proportion of the DNN model is 20％,the prediction accuracy of the tree height-diameter model can reach more than 0.89 after adding DDH factor.DNN was used to build a height-diameter model to predict the height of Cunninghamia lanceolata,and adding DDH factor could improve the prediction accuracy of Cunninghamia lanceolata height with a smaller dataset.
Cunninghamia lanceolataDNNDDHheight-diameter model
NETL
NSTL
万方数据
