基于RBF神经网络的土壤有机质空间变异研究方法
通过研究土壤性质的空间变异和空间插值方法,快速准确获取土壤性质的空间分布是精确农业和环境保护的基础。该文以四川眉山一块约40km^2的区域为试验区,采集表层土壤（0～20cm）样点80个,利用径向基函数（RBF）神经网络建立空间坐标和邻近样点与土壤有机质间的非线性映射关系（RBF2）,模拟土壤有机质的空间分布。与普通克里法（OK）和仅以坐标为网络输入的神经网络方法（RBF1）相比,RBF2的插值精度有显著的提高;相同样点密度下其相对预测误差分别较OK和RBF1减小了9.87%、1.97%（样本A）和13.09%、2.36%（样本B）;即使样点数减半的情况下RBF2的相对预测误差也分别较OK和R...
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| Published in | Nong ye gong cheng xue bao Vol. 26; no. 1; pp. 87 - 93 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
四川农业大学资源环境学院,雅安,625014
2010
中国科学院研究生院,北京,100039%四川农业大学资源环境学院,雅安,625014%中国科学院地理科学与资源研究所,北京,100101 中国科学院地理科学与资源研究所,北京,100101 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.3969/j.issn.1002-6819.2010.01.015 |
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| Abstract | 通过研究土壤性质的空间变异和空间插值方法,快速准确获取土壤性质的空间分布是精确农业和环境保护的基础。该文以四川眉山一块约40km^2的区域为试验区,采集表层土壤（0～20cm）样点80个,利用径向基函数（RBF）神经网络建立空间坐标和邻近样点与土壤有机质间的非线性映射关系（RBF2）,模拟土壤有机质的空间分布。与普通克里法（OK）和仅以坐标为网络输入的神经网络方法（RBF1）相比,RBF2的插值精度有显著的提高;相同样点密度下其相对预测误差分别较OK和RBF1减小了9.87%、1.97%（样本A）和13.09%、2.36%（样本B）;即使样点数减半的情况下RBF2的相对预测误差也分别较OK和RBF1减小了10.23%和2.33%,并且插值图差异相对较小,可以更好地反映土壤有机质空间分布的异质性。因此,利用以坐标和邻近样点为输入的神经网络方法可以相对准确、快速地获取区域土壤性质空间分布的异质性信息。 |
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| AbstractList | 通过研究土壤性质的空间变异和空间插值方法,快速准确获取土壤性质的空间分布是精确农业和环境保护的基础。该文以四川眉山一块约40km^2的区域为试验区,采集表层土壤（0～20cm）样点80个,利用径向基函数（RBF）神经网络建立空间坐标和邻近样点与土壤有机质间的非线性映射关系（RBF2）,模拟土壤有机质的空间分布。与普通克里法（OK）和仅以坐标为网络输入的神经网络方法（RBF1）相比,RBF2的插值精度有显著的提高;相同样点密度下其相对预测误差分别较OK和RBF1减小了9.87%、1.97%（样本A）和13.09%、2.36%（样本B）;即使样点数减半的情况下RBF2的相对预测误差也分别较OK和RBF1减小了10.23%和2.33%,并且插值图差异相对较小,可以更好地反映土壤有机质空间分布的异质性。因此,利用以坐标和邻近样点为输入的神经网络方法可以相对准确、快速地获取区域土壤性质空间分布的异质性信息。 S153.6~+21%TP183; 通过研究土壤性质的空间变异和空间插值方法,快速准确获取土壤性质的空间分布是精确农业和环境保护的基础.该文以四川眉山一块约40km~2的区域为试验区,采集表层土壤(0～20cm)样点80个,利用径向基函数(RBF)神经网络建立空间坐标和邻近样点与土壤有机质间的非线性映射关系(RBF2),模拟土壤有机质的窄间分布.与普通克里法(OK)和仅以坐标为网络输入的神经网络方法(RBF1)相比,RBF2的插值精度有显著的提高;相同样点密度下其相对预测误差分别较OK和RBF1减小了9.87%、1.97%(样本A)和13.09%、2.36%(样本B);即使样点数减半的情况下RBF2的相对预测误差也分别较OK和RBF1减小了10.23%和2.33%,并且插值图差异相对较小,可以更好地反映土壤有机质空间分布的异质性.因此,利用以坐标和邻近样点为输入的神经网络方法可以相对准确、快速地获取区域土壤性质空间分布的异质性信息. |
| Abstract_FL | Fast and accurate simulation of the spatial distribution of soil properties from the study on soil spatial variability and spatial interpolation was the basis for precision agriculture and environmental protection. In this paper, 80 topsoil samples were collected in a 40 km~2 test area in Meishan, Sichuan Province. Nonlinear mapped relations between spatial coordinates and neighbor samples and the content of soil organic matter were established based on radial basis function neural network (RBF2) to simulate the distribution of the content of soil organic matter in the test area. Compared with ordinary kriging method (OK) and radial basis function neural network method only using spatial coordinates as inputs of net (RBF1), the predicted errors achieved by RBF2 were much smaller, which were reduced by 9.87%, 13.09% and 1.97%, 2.36%, respectively;even samples were cut in half, the predicted error was still reduced by 10.23% and 2.33%, respectively, compared with OK and RBF1 which used in all samples. Besides, RBF2, which was able to make the interpolation maps and had smaller difference comparatively in different samples, could express the spatial heterogeneity of soil organic matter well. Thus, the spatial heterogeneity information of soil properties could be achieved exactly and quickly by the method of radial basis function neural network which used spatial coordinates and neighbor samples information as inputs of net. |
| Author | 李启权 王昌全 岳天祥 李冰 杨娟 |
| AuthorAffiliation | 四川农业大学资源环境学院,雅安625014 中国科学院地理科学与资源研究所,北京100101 中国科学院研究生院,北京100039 |
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| Author_FL | Wang Changquan Li Bing Yue Tianxiang Yang Juan Li Qiquan |
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| Keywords | 径向基函数网络 普通克里格 ordinary kriging error analysis 有机质 radial basis function networks organic matter spatial heterogeneity 空间异质性 soils 误差分析 土壤 |
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| SubjectTerms | 土壤 径向基函数网络 普通克里格 有机质 空间异质性 误差分析 |
| Title | 基于RBF神经网络的土壤有机质空间变异研究方法 |
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