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中国农学通报 ›› 2013, Vol. 29 ›› Issue (23): 210-215.doi: 10.11924/j.issn.1000-6850.2013-0010

所属专题: 耕地保护

• 农业科技信息 • 上一篇    下一篇

基于GS-SVR的耕地面积预测及其驱动因子分析

王笑冰 张红燕 谢元瑰 陈玉峰 隆轲   

  • 收稿日期:2013-01-05 修回日期:2013-01-29 出版日期:2013-08-15 发布日期:2013-08-15
  • 基金资助:
    国家科技支撑计划重大项目(农村物联网基础平台共性关键技术研究);湖南省研究生科研创新项目(时间序列分析方法在农业虫害预测中的应用研究)

Cultivated Land Prediction and Its Driving Force Analysis Base on GS-SVR

  • Received:2013-01-05 Revised:2013-01-29 Online:2013-08-15 Published:2013-08-15

摘要: 影响耕地面积变化的驱动因子复杂多变,难以确定。为了合理选择耕地面积的驱动因子,提高耕地面积的预测精度,指导耕地资源科学分配利用,通过采用一种基于GS-SVR自变量全组合预测均方误差(Mean Squared Error,MSE)最小原则的方法确定耕地面积的驱动因子。并以湖南省耕地面积变化为例,通过SVR-CAR、LSSVM、BPNN、ARIMA和MLRR等常用的时间序列预测方法来验证所选取驱动因子的有效性。结果表明,湖南省耕地面积变化的最优驱动因子组合为城市化水平和房地产业产值指数,且常用时间序列预测方法采用GS-SVR全组合方式选取的驱动因子组合大幅度提高了耕地面积的预测精度。采用GS-SVR自变量全组合均方误差最小原则的方法选择耕地面积的驱动因子是科学合理的,在耕地面积等时间序列预测领域具有广泛的应用前景。

关键词: 免疫原性, 免疫原性

Abstract: It is complicated and difficult to determine the driving factors which affect changes of cultivated land area. In order to select the effective driving factors and promote the prediction accuracy of cultivated land area, this paper proposed a method to determine the driving factors of cultivated land area relying on the minimum mean squared error (MSE) of prediction value and observed value among all combinations of independent variables based on GS-SVR. Then, taking Hunan province as a case, this paper used several time-series forecasting methods, such as SVR-CAR, LSSVM, BPNN, ARIMA and MLRR to evaluate the performance of the selected driving factors. The results showed that the optimal driving factors for cultivated land changes was the combination of urbanization level and production index of the real estate industry, and with the driving factors selected by GS-SVR, all reference methods greatly improved the prediction accuracy of cultivated land area. The proposed method has an extensive application prospect for predictions involving multidimensional time series data, such as changes of cultivated land area.