Dynamic modeling of groundwater pollutants with bayesian networks
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
DYNAMIC MODELING OF GROUNDWATER POLLUTANTS WITH BAYESIAN NETWORKS
Applied Artificial Intelligence
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There has been an increasing interest in the monitoring and the assessment of surface and groundwater quality. Experts in this area have been arguing that the current used techniques are not accurate means of measuring water contamination. This is mainly because these techniques neglect the characteristics that are significant in understanding of pollution-generation processes, which is stochastic in nature, from various sources. In particular, 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 the application of a prototype Bayesian Belief Network (BBN) that models groundwater quality in order to assess and predict the impact of pollutants on the water column. The methods presented are widely applicable and handle many of the problems encountered with other methods.