Discipline: Technology and Engineering
Subcategory: Environmental Engineering
Elvis B. Addai - North Carolina A&T States University
Co-Author(s): Shoou-Yuh Chang, North Carolina A&T States University, Greensboro, NC
In modeling the behavior of contaminants in a subsurface environment using data assimilation schemes, accurate assignment of model and observation errors are significant for the successful application of the techniques. In this study, a three-dimensional numerical model was used to simulate the advection and dispersion transport of contaminant in the subsurface. Stochastic data assimilation schemes were coupled with the subsurface contaminant transport model to predict the state of the contaminant. Three data assimilation techniques namely the Ensemble Kalman filter, Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter were adopted to improve the prediction of the contaminant fate and transport in the groundwater. The Ensemble Kalman Filter applies a Monte Carlo approach to the filtering problem. The adaptive filtering technique employs the diagnostic approach to fine tune the model and observation covariance matrix. The hybrid technique uses combination of the forecast covariance matrix and the invariant background covariance matrix to explore the Ensemble Kalman filter. The impact of the filters on the numerical model are examined by using the normalized root mean square error (NRMSE), average absolute bias (AAB)
metric, and maximum absolute deviation (MAD) techniques. The results of simulations show that the prediction accuracy of the filters is better than numerical model. The proposed Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter takes advantage of the adaptive factor and the weighting factor, respectively to improve the prediction efficiency of the Ensemble Kalman filter.
Funder Acknowledgement(s): This work was sponsored by the Department of Energy Samuel Massie Chair of Excellence Program under grant number DF-FG01-94EW11425.
Faculty Advisor: Shoou-Yuh Chang, email@example.com