文献类型：期刊文献

中文题名：Stand basal area modelling for Chinese fir plantations using an artificial neural network model

英文题名：Stand basal area modelling for Chinese fir plantations using an artificial neural network model

作者：Shaohui Che[1,2] Xiaohong Tan[1] Congwei Xiang[1] Jianjun Sun[3] Xiaoyan Hu[1] Xiongqing Zhang[1] Aiguo Duan[1] Jianguo Zhang[1]

第一作者：Shaohui Che

机构：[1]Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration,Research Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,People’s Republic of China;[2]Beijing Institute of Economics and Management,Beijing 100091,People’s Republic of China;[3]Subtropical Forestry Experimental Central,Chinese Academy of Forestry,Fenyi 336600,People’s Republic of China

年份：2019

卷号：30

期号：5

起止页码：1641-1649

中文期刊名：林业研究：英文版

收录：CSTPCD;;Scopus;CSCD:【CSCD2019_2020】；

基金：supported by the National Scientific and Technological Task in China(Nos.2015BAD09B0101,2016YFD0600302);National Natural Science Foundation of China(No.31570619);the Special Science and Technology Innovation in Jiangxi Province(No.201702)

语种：英文

中文关键词：Chinese;fir;Basal;area;Artificial;neural;network;Support;vector;machine;Mixed-effect;model

外文关键词：Chinese fir;Basal area;Artificial neural network;Support vector machine;Mixed-effect model

分类号：S

摘要：Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.
Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function, non-Gaussian distributions, multicollinearity,outliers and noise in the data. The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses. According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province, a back-propagation artificial neural network model(BPANN) and a support vector machine model(SVM) for basal area of Chinese fir(Cunninghamia lanceolata) plantations were constructed using four kinds of prediction factors, including stand age, site index, surviving stem numbers and quadratic mean diameters. Artificial intelligence methods, especially SVM, could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models. SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.