基于最优权重的落叶松单木叶面积组合预测模型
Optimal weighted combinatorial forecasting mod el of tree leaf area of Larix olgensis
郭孝玉 1余坤勇 2李增禄 1陈春乐 1刘健1
作者信息
- 1. 福建省资源环境监测与可持续经营利用重点实验室, 福建三明365004;三明学院资源与化工学院, 福建三明365004
- 2. 福建农林大学林学院,福建福州350002;3S技术与资源优化利用福建省高校重点实验室,福建福州350002
- 折叠
摘要
最优权重组合预测模型是将各种模型组合起来并分配它们适当的权重系数进行组合预测的模型,减少单项模型预测的风险性,提高预测精度.以落叶松单木叶面积为例,通过拟合一元线性、 多元非线性和多元线性等各种单项基础模型,构建最优权重组合预测模型.结果表明,胸径是预测落叶松单木叶面积的最佳变量,增加树冠率或高径比可提高模型解释力,改进异速生长方程是最佳单项模型,R2达0.927;最优权重算法组合模型优于单项模型及平均值组合模型,落叶松叶面积最优权重组合模型的估测值与实测值之间的平均绝对相对误差和均方根误差均低于单项模型,R2达0.930.构建的最优权重组合预测模型适合估测落叶松单木叶面积,估测精度高,可应用于长白落叶松人工林叶面积指数估测.
Abstract
Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. Fifty five larch ( Larix olgensis) trees were collected randomly at the Xiaoxing′an Mountains in Northeast China for fitting the univariate ( or multivariate) variable nonlinear and linear leaf area model, and the optimal weights of combination forecasting model determined by the optima tool box in Matlab 2010b software.Results showed diameter at breast height (D1.3) is the best prediction variable for estimating larch leaf area.The modified allometric equation with adding the ratio of tree height to D1.3 , with a higher explanation ability ( R2=0.927) .The index of accuracy evaluation for optimal weighted combinatorial model ( OW) was better than the single model and the mean weighted combinatorial model, and the OW-M3M8M12 forecasting model improved accuracy for predicting leaf area of individual tree.The optimal weighted combination forecasting model suit for estimating leaf area.The leaf area model would be used for estimating the leaf area index for pure larch plantation.
关键词
组合预测模型/最优权重/落叶松人工林/叶面积/异速生长方程Key words
ensemble forecast model/optimal weight/larch plantation/leaf area/allometric equation引用本文复制引用
基金项目
"十三五"国家重点研发计划项目(2017YFD0600901-3)
福建省高校产学合作重大项目(2015N5010)
三明学院引进高层次人才科研启动经费(16YG03)
S技术与资源优化利用福建省高校重点实验室开放课题()
出版年
2018
国家科技期刊平台
NSTL
维普
万方数据