scholarly journals Application of Geostatistical Models for Estimating Spatial Variability of Rock Depth

Engineering ◽  
2011 ◽  
Vol 03 (09) ◽  
pp. 886-894 ◽  
Author(s):  
Pijush Samui ◽  
Thallak G. Sitharam
Keyword(s):  
Spatial Variability ◽  
Geostatistical Models ◽  
Rock Depth

Spatial Variability of Rock Depth Using Simple Kriging, Ordinary Kriging, RVM and MPMR

2014 ◽  
Vol 33 (1) ◽  
pp. 69-78 ◽  
Author(s):  
R. Viswanathan ◽  
J. Jagan ◽  
Pijush Samui ◽  
P. Porchelvan
Keyword(s):  
Spatial Variability ◽  
Ordinary Kriging ◽  
Simple Kriging ◽  
Rock Depth

Determination of Spatial Variability of Rock Depth of Chennai

2021 ◽  
pp. 98-115
Author(s):  
Pijush Samui ◽  
Viswanathan R. ◽  
Jagan J. ◽  
Pradeep U. Kurup
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Spatial Variability ◽  
Ordinary Kriging ◽  
The Other ◽  
Generalized Regression Neural Network ◽  
Modeling Techniques ◽  
Rock Depth ◽  
Minimax Probability Machine Regression

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.

Determination of Spatial Variability of Rock Depth of Chennai

2018 ◽  
pp. 462-479 ◽  
Author(s):  
Pijush Samui ◽  
Viswanathan R. ◽  
Jagan J. ◽  
Pradeep U. Kurup
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Spatial Variability ◽  
Ordinary Kriging ◽  
The Other ◽  
Generalized Regression Neural Network ◽  
Modeling Techniques ◽  
Rock Depth ◽  
Minimax Probability Machine Regression

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.

scholarly journals Spatial dependence in experiments of progeny selection for bean ( Phaseolus vulgaris L.) yield

Revista CERES ◽  
2016 ◽  
Vol 63 (4) ◽  
pp. 477-485
Author(s):  
Michele Jorge da Silva ◽  
Antonio Policarpo Souza Carneiro ◽  
Andréia Luiza Gonzaga Feres ◽  
José Eustáquio Souza Carneiro ◽  
Nerilson Terra Santos ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Field Experiments ◽  
Model Parameters ◽  
Ratio Test ◽  
Geostatistical Model ◽  
Breeding Programs ◽  
Lattice Design ◽  
Geostatistical Models ◽  
Watson Test

ABSTRACT In field experiments, it is often assumed that errors are statistically independent, but not always this condition is met, compromising the results. An inappropriate choice of the analytical model can compromise the efficiency of breeding programs in preventing unpromising genotypes from being selected and maintained in the next selection cycles resulting in waste of time and resources. The objective of this study was to evaluate the spatial dependence of errors in experiments evaluating grain yield of bean progenies using analyses in lattice and randomized blocks. And also evaluate the efficiency of geostatistical models to describe the structure of spatial variability of errors. The data used in this study derived from experiments arranged in the lattice design and analyzed as lattice or as randomized blocks. The Durbin-Watson test was used to verify the existence of spatial autocorrelation. The theoretical semivariogram was fitted using geostatistical models (exponential, spherical and Gaussian) to describe the spatial variability of errors. The likelihood ratio test was applied to assess the significance of the geostatistical model parameters. Of the eight experiments evaluated, five had moderate spatial dependence for the randomized blocks analysis and one for both analyses, in lattice and randomized blocks. The area of the experiments was not a determinant factor of the spatial dependence. The spherical, exponential and Gaussian geostatistical models with nugget effect were suitable to represent the spatial structure in the randomized block analysis. The analysis in lattice was efficient to ensure the independence of errors.

Spatial variability induces generalization in contextual cueing.

2020 ◽  
Vol 46 (12) ◽  
pp. 2295-2313
Author(s):  
Yoko Higuchi ◽  
Yoshiyuki Ueda ◽  
Kazuhisa Shibata ◽  
Jun Saiki
Keyword(s):  
Spatial Variability ◽  
Contextual Cueing
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