Abstract
AbstractGroundwater contaminant transport modeling is basically performed to predict contaminant concentration and to understand the biochemical and physical processes that happen in the subsurface of porous media. Modelers have been faced with the challenge of accurately modeling the behavior and fate of contaminants in groundwater with models and techniques that incorporate the appropriate noise statistics and estimates the hydrogeologic parameters effectively. Unaccounted noise and uncertainties in the modeling greatly affect the accuracy of these predictions. In this paper, two Monte Carlo-based techniques, particle filter (PF) and Ensemble Kalman filter (EnKF), were applied to a three-dimensional (3D) groundwater contaminant transport model to accurately estimate the first-order decay rate and contaminant concentration at each time step. The PF and EnKF are embedded with Sampling Importance Resampling (SIR) and Singular Value Decomposition (SVD) concepts to avoid degeneracy and matrix singularity, re...
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
