文献类型：期刊文献

中文题名：基于多层感知机的长白落叶松人工林林分生物量模型

英文题名：Stand biomass model of Larix olgensis plantations based on multi-layer perceptron networks

作者：徐奇刚[1,2] 雷相东[1,2] 国红[1,2] 李海奎[1,2] 李玉堂[3]

第一作者：徐奇刚

机构：[1]中国林业科学研究院资源信息研究所;[2]国家林业和草原局森林经营与生长模拟实验室;[3]吉林省林业调查规划院

年份：2019

卷号：0

期号：5

起止页码：97-107

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：林业行业公益性科研项目(201504303)

语种：中文

中文关键词：长白落叶松;林分生物量;对数转化后线性回归模型;多层感知机模型

外文关键词：Larix olgensis;stand biomass;log-transformed linear regression model;multi-layer perceptron networks

分类号：S718.5

摘要：【目的】神经网络模型能避免林分生物量模型建模时自变量共线性与异方差问题,研究多层感知机在林分生物量模型中的应用,为森林经营单位、区域生物量和碳储量的估算提供方法和依据。【方法】以长白落叶松人工林为研究对象,利用吉林省一类清查固定样地的917组数据,分别建立了基于传统的对数转化后线性模型和神经网络多层感知机的地上生物量和总生物量模型。使用AIC、决定系数(R^2)、均方根误差(RMSE)、相对均方根误差(RMSEr)和平均绝对误差(MAE)来评价模型。【结果】估计精度最高的模型是输入单元为林分平均胸径(D)-平均高(H)-林分密度指数(S)-海拔(HB)-坡向(PX)-坡位(PW)、2个隐藏层、隐单元数为40-20的神经网络模型,与传统对数转换线性回归模型相比,地上生物量和总生物量模型的调整决定系数(Adj.R^2)分别从0.902 1提高到了0.914 1,从0.897 9提高到了0.908 9;RMSEr分别从6.330 5%降低到了5.992 2%,从6.490 1%降低到了6.153 6%。包含立地因子的神经网络模型比未包含立地因子的神经网络模型估计精度略有提升,地上生物量与总生物量的Adj.R^2分别提高了0.88%和0.99%,RMSEr分别降低了5.33%和5.46%。【结论】多层感知机生物量模型的估计精度比传统回归模型略有提高,但它可以避免模型选型和违背传统统计假设的处理等问题,且能够一次性计算地上生物量和总生物量模型,有一定优势。
[Objective]Neural network model can avoid the collinearity and heteroscedasticity of variables in modeling forest stand biomass.This paper aims to apply multi-layer perceptron networks to forest biomass model to provide methods for the calculation and prediction of forest biomass and carbon stocks at forest management unit and regional levels.[Method]Based on 917 observations from the sample plots of larch plantations from national forest inventory in Jilin Province of northeastern China,the aboveground and total biomass models by log-transformed linear regression,and multi-layer perceptron networks with and without site factors were established.AIC,adjusted R^2,RMSE,RMSEr and MAE were used to evaluate the models.[Result]The model with the highest prediction accuracy was the neural network one with the input unit quadratic mean diameters(D),stand mean height(H),stand density index(S),altitude(HB),slope(PD),slope aspect(PX),slope position(PW),two hidden layers and hidden unit number of 40-20.Compared with traditional log-transformed linear regression model,the adjusted R^2 of the aboveground and total biomass models was increased from 0.902 1 to 0.914 1,and from 0.897 9 to 0.908 9,RMSEr was decreased from6.330 5%to 5.992 2%,and from 6.490 1%to 6.153 6%,respectively.The neural network model with site factors had slightly higher estimation accuracy than that without site factors.The adjusted R^2 of the aboveground and total biomass models was increased by 0.88%and 0.99%,and RMSEr decreased by 5.33%and 5.46%,respectively.[Conclusion]Biomass model based on multi-layer perceptron networks had similar performance in terms of model accuracy,but it could avoid treating the traditional assumptions such as the collinearity and heteroscedasticity of variables,and had the ability to calculate aboveground and total biomass models at one time.