基于气象因子的烟草普通花叶病毒非线性预测模型
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摘要
研究烟草病害预测方法,可提前预警病害的发生,为制定综合治理方案以及农药合理施用方案等提供有效指导。研究首先引入非线性关联测度方法(Maximal information coefficient, MIC),并以此筛选与烟草普通花叶病毒病相关的气象因子;进一步以支持向量机(Support Vector Machine,SVM)多轮末位汰选进行自变量精细筛选;然后以地统计学(Geostatistics,GS)确定公用变程;对每一个预测样本都从训练集中找出距离小于公用变程的k个近邻,以SVR训练建模完成个体化预测。结果表明:基于k近邻的预测模型独立测试结果明显优于基于全部训练样本参与的预测模型的独立测试精度,且基于私有最近邻样本的个性化预测模型结果最优。
Predication methods are very useful to the early warning and plan making for comprehensive treatment and reasonable chemical control of tobacco diseases. A nonlinear prediction model of tobacco mosaic virus based on meteorological factors was set in our study. Firstly, the maximal information coefficient(MIC) was introduced to select the meteorological factors that associated with tobacco mosaic virus. Secondly, the meteorological factors were removed nonlinearly using support vector machine(SVM). Thirdly, a common range was confirmed using geostatistics. Fourthly, K nearest neighbors of each test sample were found from the training set with their distances shorter than common range, then the models were constructed and the individualized prediction was feasible using SVM.The results showed that the KNN models performed better than the model using all training samples. Furthermore, the model based on individualized nearest neighbor sample performed best among the KNN models.
Predication methods are very useful to the early warning and plan making for comprehensive treatment and reasonable chemical control of tobacco diseases. A nonlinear prediction model of tobacco mosaic virus based on meteorological factors was set in our study. Firstly, the maximal information coefficient(MIC) was introduced to select the meteorological factors that associated with tobacco mosaic virus. Secondly, the meteorological factors were removed nonlinearly using support vector machine(SVM). Thirdly, a common range was confirmed using geostatistics. Fourthly, K nearest neighbors of each test sample were found from the training set with their distances shorter than common range, then the models were constructed and the individualized prediction was feasible using SVM.The results showed that the KNN models performed better than the model using all training samples. Furthermore, the model based on individualized nearest neighbor sample performed best among the KNN models.
引文
[1]张兆扬,陈征,李朋彦,等.现代烟草农业背景下的烟农专业合作社发展浅析[J].湖南农业科学,2015(6):96-99.
[2]巢进,张战泓,田峰,等烟草黑胫病防控技术研究进展[J].湖南农业科学,2016(8):120-122.
[3]刘喜平.小菜蛾的发生规律及预测预报[J].湖南农业科学,2011(13):97-99.
[4]徐镜善,王凯,袁哲明.多维时间序列因变量的快速定阶及其害虫发生量预测[J].湖南农业科学,2013(19):64-67.
[5]李静.林业典型病虫害的分析与预测[D].成都:电子科技大学,2017.
[6]孙朴,胥德梅.气象因子对水稻纹枯病的预测模型研究[J].安徽农业科学,2013(23):9873-9874.
[7]Reshef D N,Reshef Y A,Finucane H K,et al.Detecting novel associations in large data sets[J].Science,2011,334(6062):1518-1524.
[8]孙丛苇,班晓琳,文鹏卓,等.基于支持向量回归预测气象因子对苎麻产量的影响研究[J].湖南农业科学,2013(21):66-69.
[9]Vapnik V,Levin E,Lecun Y.Measuring the VC-dimension of a learning machine[J].Neural Computation,1994,6(5):851-876.
[10]Chang C C,Lin C J.LIBSVM:a library for support vector machines[EB/OL].http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf.
[11]Zheng W F,Tropsha A.Novel variable selection quantitative StructureProperty relationship approach based on the k-nearest-neighbor principle[J].Journal of Chemical Information and Computer Sciences,2000,40(1):185-194.
[12]于苹苹,倪建成,韦锦涛,等.基于Spark与词语相关度的KNN文本分类算法[J].计算机技术与发展,2018,28(3):87-92.
[13]李哈滨,王政权,王庆成.空间异质性定量研究理论与方法[J].应用生态学报,1998,9(6):651-657.
[14]Liebhold A M,Rossi R E,Kemp W P.Geostatistics and geographic information systems in applied insect ecology[J].Annual Review of Entomology,1993,38(1):303-327.
[2]巢进,张战泓,田峰,等烟草黑胫病防控技术研究进展[J].湖南农业科学,2016(8):120-122.
[3]刘喜平.小菜蛾的发生规律及预测预报[J].湖南农业科学,2011(13):97-99.
[4]徐镜善,王凯,袁哲明.多维时间序列因变量的快速定阶及其害虫发生量预测[J].湖南农业科学,2013(19):64-67.
[5]李静.林业典型病虫害的分析与预测[D].成都:电子科技大学,2017.
[6]孙朴,胥德梅.气象因子对水稻纹枯病的预测模型研究[J].安徽农业科学,2013(23):9873-9874.
[7]Reshef D N,Reshef Y A,Finucane H K,et al.Detecting novel associations in large data sets[J].Science,2011,334(6062):1518-1524.
[8]孙丛苇,班晓琳,文鹏卓,等.基于支持向量回归预测气象因子对苎麻产量的影响研究[J].湖南农业科学,2013(21):66-69.
[9]Vapnik V,Levin E,Lecun Y.Measuring the VC-dimension of a learning machine[J].Neural Computation,1994,6(5):851-876.
[10]Chang C C,Lin C J.LIBSVM:a library for support vector machines[EB/OL].http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf.
[11]Zheng W F,Tropsha A.Novel variable selection quantitative StructureProperty relationship approach based on the k-nearest-neighbor principle[J].Journal of Chemical Information and Computer Sciences,2000,40(1):185-194.
[12]于苹苹,倪建成,韦锦涛,等.基于Spark与词语相关度的KNN文本分类算法[J].计算机技术与发展,2018,28(3):87-92.
[13]李哈滨,王政权,王庆成.空间异质性定量研究理论与方法[J].应用生态学报,1998,9(6):651-657.
[14]Liebhold A M,Rossi R E,Kemp W P.Geostatistics and geographic information systems in applied insect ecology[J].Annual Review of Entomology,1993,38(1):303-327.