Extended Kalman Filtering to Improve the Accuracy of a Subsurface Contaminant Transport Model

S.-Y. Chang and S.M.I. Latif (USA)


Stochastic process, dynamic system, and contaminant transport


Modeling the behavior of contaminants during the subsurface flow through soil 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, we need observation data to guide the deterministic system model to assimilate true state of contaminant. An Extended Kalman Filter (EKF), which is essentially a first order approximation to an infinite dimensional problem, is used 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 experiment shows that EKF can reduce 74 to 91% of prediction errors compare to the numerical method while working with the full set of observation data and comparing to the analytical solution.

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