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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.
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.
A Comparative Study of Ordinary Kriging and Support Vector Machine Models for the Spatial Variability of Rock Depth in Bangalore
Samui, Pijush (author) / Sitharam, T. G. (author)
GeoCongress 2008 ; 2008 ; New Orleans, Louisiana, United States
GeoCongress 2008 ; 934-941
2008-03-07
Conference paper
Electronic Resource
English
| British Library Conference Proceedings | 2008
Spatial Variability of Rock Depth Using Simple Kriging, Ordinary Kriging, RVM and MPMR
| British Library Online Contents | 2015
| British Library Online Contents | 2008
Spatial Variability of Rock Depth Using Simple Kriging, Ordinary Kriging, RVM and MPMR
| Online Contents | 2014