Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wu-Seng, L.: Water Quality Modeling. CRC Press, Inc., Boca Raton (1993)
Anderson, M., Woessner, W.: Applied Groundwater Modeling. Academic Press, London (1992)
Trigg, D.S., et al.: A prototype Bayesian belief network for diagnosis of acidification in Welsh rivers. Computer Techniques in Environmental Studies, 163-172 (2000)
Chong, H.G., Walley, W.J.: Rule-based versus Probabilistic Approaches to the Diagnosis of Faults in Wastewater Processes. Artificial Intelligence in Engineering 10, 265–273 (1996)
Varis, O.: Belief networks for modeling and assessment of environmental change. Environmetrics 6, 439–444 (1995)
Nicholson, A.E., Brady, J.M.: Dynamic Belief Networks for Discrete Monitoring. IEEE Transactions on Systems, Man, and Cybernetics}Â 24(11) (1994)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Inc., Englewood Cliffs (1995)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
HUGIN Expert Brochure, HUGIN Expert A/S, P. O. Box 8201 DK-9220, Aalborg, Denmark (2002)
Banerjee, A.K., et al.: TR no. 773, Department of Statistics, University of Wisconsin (1985)
Jensen, F.V.: Bayesian Networks and decision Diagrams. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shihab, K., Al-Chalabi, N. (2004). A Bayesian Framework for Groundwater Quality Assessment. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-24677-0_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
eBook Packages: Springer Book Archive