Use of a bayesian network decision tool for water quality assessment

Abstract

Groundwater resources in Oman are considered very precious and play a great role in the economical development. However, groundwater contamination is one of the major concerns facing the country and therefore, it needs an accurate measurement technique. In this work, the Bayesian Technique is applied to groundwater quality data sets obtained from various locations in the Salalah area to the south of Oman. This technique emphasizes not only the probabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. 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.