Dynamic Modeling of Ground‐Water Quality Using Bayesian Techniques1

: Water industry experts have been arguing that the traditional techniques are not an accurate means of measuring water contamination. This is mainly because these techniques emphasize neither the stochastic nature of the water contamination process nor the precision and the accuracy of the tested methods used by environmental laboratories. In this work, we describe the development and application of prototype Dynamic Bayesian Networks (DBNs) that model ground‐water quality to determine the impact of chemical contaminants on ground‐water quality in the Salalah area, which is allocated to the south of Oman. We also present a new technique for data pre‐processing because it is needed for the treatment of ground‐water datasets that are used as the data source to learn the probabilities for dynamic decision models. Among more than 20 wells in area, only four wells were selected to be analyzed and the results show that we achieved an acceptable level of efficiency.