Extended Kalman Filtering to Improve the Accuracy of a Subsurface Contaminant Transport Model
Publication: Journal of Environmental Engineering
Volume 136, Issue 5
Abstract
Modeling the behavior of contaminants in a subsurface flow is important in predicting the fate of the pollutants, in risk assessment, and as a preliminary step of the mitigation process. A two-dimensional transport model with advection and dispersion is used as the deterministic model of a conservative contaminant transport in the subsurface. With the system model alone, it is very difficult to predict the true dynamic state of the pollutant. Therefore, observation data are needed to guide the deterministic system model to assimilate the true state of the contaminant. Extended Kalman Filter (EKF), which is essentially a first order approximation to an infinite dimensional problem, is used in this study to predict the contaminant plume transport. A traditional root mean square error (RMSE) of pollutant concentrations is used to examine the effectiveness of the EKF in contaminant transport modeling. The result shows that EKF can reduce 74 to 91% of prediction errors compared to the numerical method while working with the full set of observation data and using the analytical solution as the true solution. It can reduce 24 to 90% of prediction errors while working with only 2.25% observation-site data and a simulated random true field.
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Acknowledgments
This work was sponsored by the Department of Energy Samuel Massie Chair of Excellence Program under Grant No. UNSPECIFIEDDF-FG01-94EW11425. 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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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 136 • Issue 5 • May 2010
Pages: 466 - 474
Copyright
© 2010 ASCE.
History
Received: Mar 4, 2009
Accepted: Oct 14, 2009
Published online: Oct 23, 2009
Published in print: May 2010
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