A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

农田土壤特性的空间变异性及Kriging估值法

以自相关和半方差函数对 40 m× 45m面积内的 90个点进行了土壤容重、田间持水量的空间变异性研究。结果表明 ,土壤特性表现出空间变异结构 ,但不是完全的随机变异 ,在一定范围内存在着空间相关现象 ,且空间结构无明显的方向 ,并对以空间变异结构为基础的 Kriging估值法作了探讨 ,其较普通平均法估值精度高。