Heavy metal pollution assessment using support vector machine in the Shur River, Sarcheshmeh copper mine, Iran
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Mining and related industries are widely considered as having unfavorable effects on environment in terms of magnitude and diversity. As a matter of fact, groundwater and soil pollution are noted to be the worst environmental problems related to the mining industry because of the pyrite oxidation, acid mine drainage generation, release and transport of the heavy metals. Acid mine drainage (AMD) containing heavy metals including Manganese (Mn), Copper (Cu), Lead (Pb), and Iron (Fe), is harmful for the human and aquatic environment. Metal pollution assessment using cost-effective methods, will be a crucial task in designing a remediation strategy. The aim of this paper is to predict the heavy metals included in the AMD using support vector machine (SVM). In addition, the obtained results are compared with those of the general regression neural network (GRNN). Results indicated that the SVM approach is faster and is more precise than the GRNN method in prediction of heavy metals. The results obtained from this paper can be considered as an easy and cost-effective method to monitor groundwater and surface water affected by AMD. © 2012 Springer-Verlag.
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