Subsurface Radioactive Contaminant Transport Modeling Using Particle and Kalman Filter Schemes

Abstract

Contamination of groundwater by radioactive contaminants can be harmful to the environment. Various prediction models have been adopted to simulate the state of contaminants in the subsurface. Conventional numerical models are simplified by approximation and the model parameters are assumed to be constant, thereby introducing error to the prediction results. Particle and Kalman filters are used in this research to simulate the radioactive contaminant cobalt-57 transport in a subsurface environment by using a two-dimensional advection-dispersion model. A radioactive contaminant concentration was predicted spatially and temporally within boundary conditions. The errors in the prediction results were assessed by using the root-mean-square-error (RMSE) equation. The results show that the Kalman filter performs better than the particle filter when the prediction model is linear. Furthermore, the results from filters are closer to the true value in comparison with the numerical solution, and the filters are capable of reducing the RMSE of the numerical solution by approximately 80%.