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土地利用规划修编中粮食产量预测方法比较
引用本文:和文超,师学义,邓青云,何 灏.土地利用规划修编中粮食产量预测方法比较[J].农业工程学报,2011,27(12):348-352.
作者姓名:和文超  师学义  邓青云  何 灏
作者单位:中国地质大学土地科学技术学院,北京,100083
基金项目:国家自然科学基金资助项目(70673055)
摘    要:为探索提高土地利用总体规划修编中粮食产量预测精度的方法,该文应用1988-2005年晋城市粮食产量相关数据的分析,对线性回归模型、灰色GM(1,1)模型和灰色多元线性回归组合模型3种粮食产量预测方法进行了研究.首先,运用灰色关联分析对影响粮食产最的影响因素作出关联因子排序;其次,在灰色关联分析的基础上选取主要影响因子;再次,利用灰色GM(1,1)模型得到主要影响因素的预测值,同时,利用原始数据建立多元线性回归模型;最后,将灰色GM(1,1)模型的预测结果作为多元线性回归模型的输入值,得到灰色多元线性回归组合模型.通过比较这3种粮食产量预测方法的预测结果,得出灰色多元线性回归组合模型最适宜于晋城市粮食产量的预测.该研究可提高土地利用总体规划编制的科学性.

关 键 词:土地利用  规划  模型  灰色关联分析  多元线性回归  粮食产量预测
收稿时间:2011/5/12 0:00:00
修稿时间:2011/8/30 0:00:00

Comparison of grain yield prediction methods in land use planning
He Wenchao,Shi Xueyi,Deng Qingyun and He Hao.Comparison of grain yield prediction methods in land use planning[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(12):348-352.
Authors:He Wenchao  Shi Xueyi  Deng Qingyun and He Hao
Abstract:In order to explore the ways of increasing grain yield prediction accuracy in land use planning, related data of grain yield was analyzed between 1988 to 2005 in Jincheng, and the linear regression model, gray GM (1,1) model and gray- multiple linear regression model was compared. Firstly, using grey incidence analysis, factors affecting the grain yield were sorted. Secondly, based on the grey incidence analysis, main factors were selected. Thirdly, through gray GM (1,1) model, the predictive value of main factors was calculated. At the same time, the multiple linear regression model was also constructed using original data. The last, taking predictions of gray GM (1,1) model as input value, gray- multiple linear regression model was constructed. The result showed that gray- multiple linear regression model had higher prediction accuracy than linear regression model and gray GM (1,1) model ,it was the most appropriate model to forecast the grain yield in Jincheng. The research could improve the scientific of establishment in land use planning.
Keywords:land use  planning  models  grey incidence analysis  multiple linear regression  forecast of grain yield
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