A Comparison Between Artificial Neural Network And A Geostatistical Technique In The Estimation Of Regionalized Variables

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A Comparison Between Artificial Neural Network And A Geostatistical Technique In The Estimation Of Regionalized Variables

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dc.contributor.author Tawo, E. E. en_US
dc.date.accessioned 2009-11-25T13:02:45Z
dc.date.available 2009-11-25T13:02:45Z
dc.date.issued 1999 en_US
dc.identifier.citation Engineering Journal of Qatar University, 1999, Vol. 12, Pages 125-149. en_US
dc.identifier.uri http://hdl.handle.net/10576/7804
dc.description.abstract In all mining estimation techniques data play a very important role. Insufficient data would mean the quality of the estimate is unreliable. Due to the importance attached to a good and representative data set, the application of Geostatistics to the mining industry puts data before any model. Obtaining a good data set which is usually from drillhole samples is an expensive experiment besides the economic constraint usually placed on the amount of data which can be collected particularly at an early stage of a mineral development. The advent of a novel technique in 'Artificial Neural Network' (ANN) and its application to the minerals industry is compared with a tested geostatistical technique. Both techniques are presented and tested on a Bauxite deposit. The effectiveness of ANN as a cost saving technique is appraised en_US
dc.language.iso en en_US
dc.publisher Qatar University en_US
dc.subject Engineering: Research Papers en_US
dc.title A Comparison Between Artificial Neural Network And A Geostatistical Technique In The Estimation Of Regionalized Variables en_US
dc.type Article en_US
dc.identifier.pagination 125-149 en_US
dc.identifier.volume 12 en_US

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