Determination of Spatial Variability of Rock Depth of Chennai

Determination of Spatial Variability of Rock Depth of Chennai

Pijush Samui, Viswanathan R., Jagan J., Pradeep U. Kurup
Copyright: © 2021 |Pages: 18
DOI: 10.4018/978-1-7998-8048-6.ch006
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Abstract

This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.
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Background

Researchers used various random field method for prediction purpose (Yaglom, 1962; Lumb, 1975; Alonso & Krizek, 1975; Vanmarcke, 1977; Tang, 1979; Wu &Wong, 1981; Tabb & Yong, 1981; Asaoka & Grivas, 1982; VanMarcke, 1998; Baecher, 1984; Baker, 1984; Kulatilake & Miller, 1987; Kulatilake, 1989; Fenton, 1998; Phoon & Kulhawy, 1999; Fenton, 1999; Uzielli et al., 2005). In random field method, the science of prediction in the presence of correlation between samples is not at all well developed. Statistical parameters contain uncertainty in random field method. In order to fill the holes of some uncertainty and also to reduce the cost, various intelligent techniques were evolved and utilized according to the requirements.

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