文献类型：期刊文献

中文题名：林分断面积组合预测模型权重确定的比较

英文题名：Comparison of Weight Computation in Stand Basal Area Combined Model

作者：张雄清[1] 雷渊才[1] 陈新美[1]

机构：[1]中国林业科学研究院资源信息研究所

年份：2011

卷号：47

期号：7

起止页码：36-41

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金："十一五"国家科技支撑计划重点项目(2006BAD23B02)

语种：中文

中文关键词：林分断面积;组合预测;误差平方和法;方差协方差法;最优加权法

外文关键词：stand basal area; forecast combination; sum of squared errors method（SSE）; variance-covariance method; optimal weight method

分类号：S758.1

摘要：引入组合预测方法以提高林分断面积预测的精度及2类模型(林分水平模型和单木水平模型)预测林分断面积的兼容性。组合预测法能够充分利用各单个模型的有效信息,从而提高预测精度,而单个模型权重的选取对提高组合预测法的精度至关重要。本研究基于北京山区油松连续清查数据,利用误差平方和法、方差协方差法和最优加权法确定林分断面积组合预测模型的权重。结果表明:组合预测法能够提高预测精度,同时利用最优加权法所建立的林分断面积组合预测模型其预测精度最高,方差协方差法次之,误差平方和法预测精度最低。
In this paper, forecast combination was introduced to improve stand basal area prediction accuracy and compatibility. A linear combination of two or more predictions may often yield more accurate forecasts than using a single model to the extent that the component forecasts contain useful and independent information. But it is very important to calculate weight coefficients for improving forecast combination. Based on the data of the Chinese pine in Beijing mountains, sum of squared errors method, variance-covariance method and optimal weight method were used to calculate weight coefficients of combined model. Results show that forecast combination for predicting stand basal area outperformed over the stand-level and tree-level models respectively, and meanwhile, stand basal area model based on the optimal weight method（R2=0.929 2） is superior to other two methods, and variance-covariance method（R2=0.929 1） is better than the sum of squared errors method（SSE）（R2=0.929 1）.