Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

SHEN Zhang-Quan; SHI Jie-Bin; WANG Ke; KONG Fan-Sheng; J. S. BAILEY

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Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

机译：神经网络集成剩余克里格法在土壤性状空间变异中的应用

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High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN)ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.

机译：高质量的农业养分分布图对于精确管理是必不可少的，但取决于初始土壤样品分析和内插技术。为了研究基于神经网络集成残差克里格法的土壤属性插值方法，并探索其插值能力，本研究选择了英国北爱尔兰海斯市的青贮场，并将所有样本分为独立的训练和验证数据集。训练数据集包括五种土壤属性：土壤pH，土壤有效磷，土壤有效钾，土壤有效镁和土壤有效硫，使用1）神经网络集成残差克里格法，2）神经网络集成和3）通过验证数据集估计克里金法的准确性。残差的普通克里金法提供了准确的局部估计，而最终估计值是人工神经网络（ANN）集合估计和残差的普通克里金法估计的总和。与克里金法和神经网络集成相比，神经网络集成残差克里金法在预测和估计等高线图方面获得了更好或相似的精度。因此，结果表明，人工神经网络集成残差克里金法是通常用于对土壤科学领域中的数据集进行插值的常规地统计学模型的有效替代方法。

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来源
《土壤圈（英文版）》 |2004年第3期|289-296|共8页
作者
SHEN Zhang-Quan; SHI Jie-Bin; WANG Ke; KONG Fan-Sheng; J. S. BAILEY;
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作者单位

Institute of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029 (China);

College of Computer Science, Zhejiang University, Hangzhou 310027 (China);

Zhejiang University Library, Hangzhou 310029 (China);

Institute of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029 (China);

College of Computer Science, Zhejiang University, Hangzhou 310027 (China);

Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science Division, Newforge Lane, Belfast BT9 5PX (UK);

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收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
原文格式 PDF
正文语种 chi
中图分类农业基础科学;
关键词
kriging; neural networks ensemble; residual; soil properties; spatial variability;

机译：克里金法;神经网络集成;残差;土壤性质;空间变异性;

Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

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