DYNAMIC MODELING OF GROUNDWATER POLLUTANTS WITH BAYESIAN NETWORKS

Abstract

The emphasis on the need to protect groundwater quality has resulted in an increased interest in groundwater quality assessment. Water experts and researchers in the area have been, however, arguing that the currently used techniques are not accurate means of measuring groundwater contamination. It is mainly because these techniques neglect not only the probabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. Therefore, this work describes the development and application of a prototype Dynamic Bayesian Network (DBN) that addresses these problems through the use of a temporal probabilistic model. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as well as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.