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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 108
Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping
Paper 281

Ensemble Contaminant Transport Modelling and Bayesian Decision-Making of Groundwater Monitoring

E.K. Paleologos1, M.T. Al Nahyan2, S. Farouk2 and K. Papapetridis3

1Department of Civil Engineering, Abu Dhabi University, United Arab Emirates
2College of Business Administration, Abu Dhabi University, United Arab Emirates
3Environmental Protection and National Projects Department, Hellenic Air Force, Greece

Full Bibliographic Reference for this paper
E.K. Paleologos, M.T. Al Nahyan, S. Farouk, K. Papapetridis, "Ensemble Contaminant Transport Modelling and Bayesian Decision-Making of Groundwater Monitoring", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 281, 2015. doi:10.4203/ccp.108.281
Keywords: water resources engineering, environmental modelling, multi-criteria decision making, stochastic optimization, stochastic mechanics.

An ensemble-based Monte Carlo framework in conjunction with a particle tracking method was used to investigate contaminant movement into the subsurface environment from an instantaneous leak emanating from a random location near the ground surface. Our findings include the following. A large number of wells, exceeding in all cases 12 monitoring wells were required in order to detect contaminants with some degree of confidence. The optimum distance that returned the maximum probability of detection Pd varied based on the heterogeneity and dispersion of the geologic medium. A low dispersive environment required larger distances in order for plumes to be able to be captured by the monitoring network, whereas high dispersive environments allowed detection close to the contamination source. Low dispersive geologic media made selection of the location of the monitoring system relatively insensitive to the distance from the source, whereas in high dispersive environments there appeared a narrow region, where the system needed to be placed in order to achieve high probabilities to detect. In highly dispersive media sampling influenced the Pd significantly. Finally, optimization of a monitoring network needs to consider concurrently the maximization of the probability of detection and the minimization of the contaminated volume.

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