Stochastic Modeling of Groundwater Contamination—A Distributed Parameter Kalman Filtering Approachby Wendy D. Graham, Univ of Florida, United States,
Dennis B. McLaughlin, Univ of Florida, United States,
Abstract: Stochastic methods are applied to the analysis and prediction of large scale solute transport in saturated heterogeneous porous media. An extended distributed parameter Kalman Filter which propagates partial differential equations for the conditional mean concentration and the conditional concentration covariance through space and time is demonstrated. At any time the conditional mean concentration represents the best (minimum variance, unbiased) estimate of the concentration plume, while the conditional concentration covariance provides an estimate of the uncertainty associated with this prediction. The filter predictions are updated when measurements become available based on the deviation of observed values of hydraulic conductivity, head and/or solute concentration from the model predictions. The performance of the filter is demonstrated using a synthetically generated concentration plume.
Subject Headings: Filters | Groundwater pollution | Stochastic processes | Parameters (statistics) | Kalman filters | Plumes | Hydrologic models
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