NDLI: Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield, South Africa

Content Provider	Springer Nature Link
Author	Tutu, H. Cukrowska, E. M. Dohnal, V. Havel, J.
Copyright Year	2005
Abstract	Mine tailings generate significant environmental impacts and contribute to water pollution. The Central Rand goldfield, South Africa is replete with gold mine tailings which have contributed significantly to water pollution as a result of acid mine drainage (AMD). Water quality is affected by mine tailings and spillages, especially from active slimes dams, currently reprocessed tailings, as well as footprints left behind after reprocessing. The release and distribution of uranium from these sites was studied. Correlation matrices show a strong link between different variables as a result of AMD produced. Principal component analysis (PCA) was used to identify very influential variables which account for the pollution trends. Artificial neural networks (ANN) using the Kohonen algorithm were applied to visualise these trends and patterns in the distribution of uranium. High concentrations of this radionuclide were detected in streams in the vicinity of the tailings dumps, active slimes and reprocessing areas. The concentrations are reduced drastically in dams and wetlands as a result of precipitation and dilution effects.
Starting Page	143
Ending Page	152
Page Count	10
File Format	PDF
ISSN	14202026
Journal	Environmental Modeling & Assessment
Volume Number	10
Issue Number	2
e-ISSN	15732967
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	uranium tailings AMD ANN Applications of Mathematics Mathematical Modeling and Industrial Mathematics Environment
Content Type	Text
Resource Type	Article
Subject	Environmental Science

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