Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data

Abstract

The detection of arsenic in sediments of placer gold mining areas is critical for planning future controls on migration and mitigation, or tapping uncontaminated groundwater resources for public water use. Arsenic (As) is often found to be collocated and correlated with gold in sediments. However, due to biogeochemical processes, arsenic can partition between the solid and the dissolved fractions in sediments and their interstitial waters. Such partitioning can mobilize arsenic into areas away from the co-located gold distribution in the sediments. In such cases, it is critical to detect the dispersed arsenic concentration. In this paper, neural network (NN) and kriging techniques were used to predict the presence of arsenic in the sediments of Circle City, Alaska using the gold concentration distribution within the sediments. The results obtained using kriging were more promising than those using NNs, albeit a statistically low correlation existed between the observed and the predicted arsenic concentrations. However, irrespective of the method used, the prediction of arsenic value without using gold concentration data was extremely poor.