Author(s)

M. Kemblowski & P.C. Johnson

Abstract

The paper addresses the problem of rational environmental monitoring, modeling, and management (Em^), and associated with it decision-making processes that are characterized by a high degree of uncertainty with respect to the input data (such as contaminant loads), fate and transport parameters (such as degradation rates), process models, and connections between such models. We propose to deal with these complex issues in the framework of probabilistic expert systems, and more specifically, Bayesian Belief Networks (BBNs). We discuss the applicability of BBNs to Em^ via a simple example related to groundwater monitoring. This is followed by an application of BBNs to a forensic subsurface contamination problem. 1. Introduction In this paper, we would like to focus on the interplay amo

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