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广西横县森林资源变化的非等间距灰色预测
引用本文:宋星旻,胡厚臻,李枫.广西横县森林资源变化的非等间距灰色预测[J].安徽农业科学,2018,46(9):1-3,7.
作者姓名:宋星旻  胡厚臻  李枫
作者单位:广西林业勘测设计院,广西南宁,530000;南宁职业技术学院,广西南宁,530008
摘    要:运用灰色预测模型理论,以1990—2014年横县少量的、不连续的森林资源指标为时间数据序列,借助MATLAB软件分析横县林地面积、森林面积(有林地)、森林覆盖率和活立木总蓄积量的变化,建立非等距灰色预测模型。根据拟合方程得到1990—2014年各指标的拟合值,除1999、2009年的活立木蓄积量拟合误差为10.14%、11.70%外,其余拟合误差都小于10.00%,拟合结果较好;检验的方差比C都小于0.35,小误差概率P=1,预测等级都为一级"好"。预测结果表明,2017—2030年森林资源除了林地面积略有减少外,其他都呈现增长的趋势,但林地面积、森林面积、森林覆盖率的相对变化率都低于1%,而活立木总蓄积量则相较于2017年相对净增长率分别为6.02%、17.15%、29.83%。说明近年来横县森林资源保护措施相对得当,森林资源保护和经营效果显著。

关 键 词:森林资源  非等间距序列  灰色模型预测  横县

Non-equidistance Gray Mode Forecast of Forest Resource Dynamic Changes in Hengxian,Guangxi
SONG Xing-min,HU Hou-zhen,LI Feng.Non-equidistance Gray Mode Forecast of Forest Resource Dynamic Changes in Hengxian,Guangxi[J].Journal of Anhui Agricultural Sciences,2018,46(9):1-3,7.
Authors:SONG Xing-min  HU Hou-zhen  LI Feng
Abstract:Using grey model theory,taking discontinuous forest resources index of Hengxian during 1990-2014 as the time serial data,non equi-distant grey prediction model was established by analyzing Hengxian forest area land,forest area(forest),forest coverage rate and total stumpage volume changes using MATLAB software.According to fitting value of each index of the fitting equation from 1990 to 2014,the fitting error of stumpage was 10.14% and 11.70% except in 1999,2009,the fitting error was less than 10.00%,the fitting result was better.The variance ratio C were less than 0.35,small error probability P=1,prediction level was one-grade.The prediction results showed that changes of forest resources form 2017 to 2030 in addition to the woodland area decreased slightly,the other was a growing trend,but the forest land area,forest area,forest coverage rate of the relative change rate was less than 1%,while net growth rates of the total stumpage volume compared to 2017 were 6.02%, 17.15%,29.83%.In recent years,Hengxian forest resources protection measures were relatively appropriate,forest resources protection and forest resources management effect was significant.
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