Uncertainty-Based Risk Classification and Scaling for Remedial Decision-Making

M.Y. Li, A.M. Michalak, P. Adriaens
University of Michigan, US

Keywords: sediments, contaminants, geostatistics

Abstract:

The interpretation of spatially distributed data in support of remedial decision-making is impaired by the challenge to differentiate between true values and noise. This challenge impacts how a contaminated area is classified using the likelihood of exceedence (of a target value) estimate, resulting in misclassification (false positive/false negative) of risk, and thus remedial cost.