Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field

The focus of this research is in the area of spatial estimation. Such a study is very important in order to improve the spatial prediction performance. Many techniques of prediction that are based on the regionalized variables, and the surface trend change from linear to quadratic or cubic that prod...

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Main Author:	Dhaher, Ghanim Mhmood
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/78037/1/GhanimMhmoodDhaherPFS2014.pdf
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id	my-utm-ep.78037
record_format	uketd_dc
spelling	my-utm-ep.780372018-07-23T06:05:44Z Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field 2014-03 Dhaher, Ghanim Mhmood QA Mathematics The focus of this research is in the area of spatial estimation. Such a study is very important in order to improve the spatial prediction performance. Many techniques of prediction that are based on the regionalized variables, and the surface trend change from linear to quadratic or cubic that produces inaccurate results in the prediction process. In this thesis, Bayesian and fuzzy kriging methods are suggested to solve the problem of uncertainty, which requires obtaining a minimum error in the prediction process. This study aims to improve the mixed approaches among methods of spatial prediction that are used for evaluation of prediction. The study also finds the performance of variation interpolation methods of minerals needed to develop the relationship between Bayesian techniques and fuzzy kriging and apply the results for further modeling a spatial relationship. This spatial prediction assumes stationary property. The findings of this study are mathematical models of covariance functions. The variogram and cross variogram functions are computed for all compass directions for the phenomena under the study and its parameters are estimated. Another aspect is to obtain Bayesian predictor, kriging predictor, and Bayesian kriging variance which represent the minimum variance of prediction. In addition, the constraints weights of linear prediction were computed. The practical side of this study includes the applications of the Bayesian and fuzzy kriging techniques on real spatial data with their locations in the mining fields of Australia, Canada, and Colombia. All the computations were carried out by using Matlab software. In conclusion, this study uses two different methods (Bayesian and fuzzy kriging techniques) for incorporating the spatial autocorrelation in order to improve the accuracy of uncertainty and estimation with minimum error. The approach combines more than one prediction methods to determine a model which is based on a cross validation that satisfies the best optimal prediction. 2014-03 Thesis http://eprints.utm.my/id/eprint/78037/ http://eprints.utm.my/id/eprint/78037/1/GhanimMhmoodDhaherPFS2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97949 phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Dhaher, Ghanim Mhmood Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
description	The focus of this research is in the area of spatial estimation. Such a study is very important in order to improve the spatial prediction performance. Many techniques of prediction that are based on the regionalized variables, and the surface trend change from linear to quadratic or cubic that produces inaccurate results in the prediction process. In this thesis, Bayesian and fuzzy kriging methods are suggested to solve the problem of uncertainty, which requires obtaining a minimum error in the prediction process. This study aims to improve the mixed approaches among methods of spatial prediction that are used for evaluation of prediction. The study also finds the performance of variation interpolation methods of minerals needed to develop the relationship between Bayesian techniques and fuzzy kriging and apply the results for further modeling a spatial relationship. This spatial prediction assumes stationary property. The findings of this study are mathematical models of covariance functions. The variogram and cross variogram functions are computed for all compass directions for the phenomena under the study and its parameters are estimated. Another aspect is to obtain Bayesian predictor, kriging predictor, and Bayesian kriging variance which represent the minimum variance of prediction. In addition, the constraints weights of linear prediction were computed. The practical side of this study includes the applications of the Bayesian and fuzzy kriging techniques on real spatial data with their locations in the mining fields of Australia, Canada, and Colombia. All the computations were carried out by using Matlab software. In conclusion, this study uses two different methods (Bayesian and fuzzy kriging techniques) for incorporating the spatial autocorrelation in order to improve the accuracy of uncertainty and estimation with minimum error. The approach combines more than one prediction methods to determine a model which is based on a cross validation that satisfies the best optimal prediction.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Dhaher, Ghanim Mhmood
author_facet	Dhaher, Ghanim Mhmood
author_sort	Dhaher, Ghanim Mhmood
title	Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
title_short	Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
title_full	Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
title_fullStr	Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
title_full_unstemmed	Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
title_sort	bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2014
url	http://eprints.utm.my/id/eprint/78037/1/GhanimMhmoodDhaherPFS2014.pdf
_version_	1747817891820994560

Bayesian and fuzzy kriging interpolation techniques for spatial estimation in mining field

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