Extended Kalman Filtering to improve the accuracy of a subsurface contaminant transport model

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. DOI: 10.1061/(ASCE)EE.1943-7870.0000179 CE Database subject headings: Stochastic processes; Pollutants; Kalman filters. Author keywords: Stochastic process; Dynamic system; Contaminant transport; Process noise; Observation noise; Extended Kalman filter (EKF).