Khalil Shihab¹ and Nida Al-Chalabi², ¹Victoria University, Australia and ²SQU, Oman

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Bayesian Belief Networks, Water Quality Assessment, Data Mining