Arsenic is often present in gold mining areas. The high sensitivity of arsenic to biogeochemical conditions may lead to catastrophic consequences through contamination of resources such as ground water. Therefore, it is critical to understand the spatial occurrence of arsenic across a given site. Previous studies using traditional pattern recognition techniques such as neural networks and kriging have not been entirely successful in predicting arsenic concentrations across a gold mining area. The methods used in this paper are the support vector machines (SVM) and robust least-square support vector machines (robust LS-SVM). The two techniques were used to predict arsenic concentrations in the sediments of Circle City, Alaska, using the gold concentration distribution present within the sediments. The analysis of the results shows an improved performance and better predictive capabilities of SVM and robust LS-SVM than that of the neural networks and kriging techniques. The robust LS-SVM performed better than the SVM. The performance of the SVM was affected by outliers. The removal of the outliers from the data set and application of SVM showed improved results.
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Twarakavi, N.K.C., Misra, D. & Bandopadhyay, S. Prediction of Arsenic in Bedrock Derived Stream Sediments at a Gold Mine Site Under Conditions of Sparse Data. Nat Resour Res 15, 15–26 (2006). https://doi.org/10.1007/s11053-006-9013-6
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DOI: https://doi.org/10.1007/s11053-006-9013-6