A discriminative-generative approach to the characterization of subsurface contaminant source zones

Large-scale contamination of ground water due to improper disposal of hazardous chemicals poses a global threat to drinking water supplies. Effective restoration and remediation of such sites relies upon a knowledge of the contaminant´s distribution within the subsurface. Obtaining a detailed map of the existing distribution is usually not feasible; rather partial knowledge in terms of certain metrics that characterize the distribution has recently been shown to be sufficient for planning and monitoring remediation strategies. In this work we explore the prediction of a representative metric based upon down-gradient concentration profiles using a classification framework where each class represents a particular sub-range of the metric. Initial experiments show that our proposed model can be used effectively for predicting the metric.