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土壤属性空间预测中变异函数套合模型的表达与参数估计
引用本文:杨 勇,李卫东,贺立源.土壤属性空间预测中变异函数套合模型的表达与参数估计[J].农业工程学报,2011,27(6):85-89.
作者姓名:杨 勇  李卫东  贺立源
作者单位:华中农业大学资源与环境学院,武汉,430070
基金项目:教育部新教师基金(20100146120018);国家自然科学基金(40971269);中央高校基本科研业务费专项资金(52204-10036和52204-07077);数字制图与国土信息应用工程国家测绘局重点实验室开放研究基金资助项目
摘    要:针对当前地统计学方法中理论变异函数的参数估计多集中于对单一理论模型的参数估计,而缺乏对多尺度套合模型参数估计的研究现状,该文首先根据多尺度套合模型的特征,提出其在计算机中的统一叠加表达形式,从经验半方差散点图判断模型类型及参数取值范围,然后用遗传算法进行参数估计,并用Matlab R2010a开发了应用程序,最后以土壤养分(有机质和全钾)为例,与当前流行的地统计软件对比,证实了所提方法不仅在理论模型参数估计精度上优于传统估值方法(对比R2,分别提高6.32%和83.47%),而且后期的Kriging插值结果的精度上更是大大优于传统方法的插值精度,证明该文所提方法在精度和套合结构支持方面均具有优势。

关 键 词:参数估计,应用程序,模型,变异函数,遗传算法
收稿时间:9/6/2010 12:00:00 AM
修稿时间:2011/5/10 0:00:00

Uniform expression of variogram nested model and parameter estimation in spatial prediction of soil properties
Yang Yong,Li Weidong and He Liyuan.Uniform expression of variogram nested model and parameter estimation in spatial prediction of soil properties[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(6):85-89.
Authors:Yang Yong  Li Weidong and He Liyuan
Affiliation:Yang Yong,Li Weidong,He Liyuan (College of Resources and Environment,HuaZhong Agricultural University,Wuhan 430070,China)
Abstract:According to the situation that the current theoretical variogram parameters estimation of the Geostatistics was mostly concentrated in single model parameter estimation, and was short of multi-scale nested model parameters estimation, a unified superimposed expression in computer was put forward based on characteristics of the nested multi-scale model. The model type and range of parameters were determined with the empirical semi-variance plot. Then parameters with genetic algorithm were estimated and the application software was developed using Matlab R2010a, and finally, the software was compared with the current popular statistical software by taking the soil nutrients (organic matter and potassium) as examples. The results showed that this method surpassed the traditional method in accuracy of the theoretical model parameters estimation(compared with R2, increased by 6.32% and 83.47% respectively), and the accuracy of the results of Kriging interpolation in later period was much better than that of the traditional ones, which proved that the method proposed in this paper had advantages in both accuracy and the support of nested structure.
Keywords:parameter estimation  application  models  semivariogram  genetic algorithm
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