Application of Sequential Data-Assimilation Techniques in Groundwater Contaminant Transport Modeling
Publication: Journal of Environmental Engineering
Volume 142, Issue 2
Abstract
Groundwater contaminant transport modeling is basically performed to predict contaminant concentration and to understand the biochemical and physical processes that happen in the subsurface of porous media. Modelers have been faced with the challenge of accurately modeling the behavior and fate of contaminants in groundwater with models and techniques that incorporate the appropriate noise statistics and estimates the hydrogeologic parameters effectively. Unaccounted noise and uncertainties in the modeling greatly affect the accuracy of these predictions. In this paper, two Monte Carlo-based techniques, particle filter (PF) and Ensemble Kalman filter (EnKF), were applied to a three-dimensional (3D) groundwater contaminant transport model to accurately estimate the first-order decay rate and contaminant concentration at each time step. The PF and EnKF are embedded with Sampling Importance Resampling (SIR) and Singular Value Decomposition (SVD) concepts to avoid degeneracy and matrix singularity, respectively. The simulation is performed with a specified domain space and with ensembles and particles size of 50 for parameter estimation and concentration prediction. A set of sparse observation points selected at specific locations were used to update the predictions from the filter at each time step. An analytical solution is generated as true solution to test the accuracy of the predicted values. Algorithms to generate the simulation results were run. The first-order decay rate estimated using PF and EnKF all converged at , which matches the actual decay rate. The performance and accuracy of the numerical method, PF, and EnKF were tested using Mean Absolute Error (MAE) and Maximum Absolute Error () equations. The results show that the EnKF performs better than both the PF and the numerical method. Also, the EnKF is capable of reducing the error in the numerical solution by approximately 85%. The R-square adjusted values for the numerical solution, PF, and EnKF were 55, 91.2, and 97.3%, respectively. This statistical value further confirms that EnKF approach performs better than PF.
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Acknowledgments
This work was sponsored by the Department of Energy Samuel Massie Chair of Excellence Program under Grant No. DE-NA0000718. The views and conclusions contained herein are those of the writers and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the funding agency.
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Published In
Journal of Environmental Engineering
Volume 142 • Issue 2 • February 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Dec 16, 2013
Accepted: Aug 11, 2015
Published online: Oct 7, 2015
Published in print: Feb 1, 2016
Discussion open until: Mar 7, 2016
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