Abstract
Pollution of groundwater can be harmful to the environment. The use of subsurface contaminant transport models, combined with stochastic data assimilation schemes, can give on-target predictions of contaminant transport to enhance the reliability of risk assessment in the area of environmental remediation. In this study, a two-dimensional transport model with advection and dispersion is used as the deterministic model of contaminant transport in the subsurface. An Adaptive Extended Kalman Filter (AEKF) is constructed as a stochastic data assimilation scheme to meliorate the prediction of the contaminant concentration. The effectiveness of the AEKF is determined by using a root mean square error (RMSE) of pollutant concentrations in contaminant transport modeling. The implementation of the AEKF was successful in improving the prediction accuracy of the deterministic model by about 60.7 % which shows a substantial improvement in the prediction of the contaminant concentration in the subsurface environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, S. Y., & Jin, A. (2005). Kalman filtering with regional noise to improve accuracy of contaminant transport models. Journal of Environmental Engineering, 131(6), 971–982.
Chang, S. Y., & Latif, S. M. I. (2010). Extended Kalman filtering to improve the accuracy of a subsurface contaminant transport model. Journal of Environmental Engineering, 136(5), 466–474.
Gerald, W. R. (2011). Finite difference approximations to the heat equation. Port-land, OR: Mechanical Engineering Department Portland State University. Retrieved August 15, 2012, from www.f.kth.se/~jjalap/numme/FDheat.pdf.
Guoquan, P. H., & Stergios, I. R. (2008). An observability constrained UKF for improving slam consistency. Multiple Autonomous Robotic Systems Laboratory, Technical Report Vol. 2, pp. 1–29.
Han, J., Kim, D., & Sunwoo, M. (2009). State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter. Journal of Power Sources, 188, 606–612.
Hide, C., Michaud, F., & Smith, M. (2004). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. IEEE Position Location and Navigation Symposium, Monterey, CA, 227–233.
Hu, C., Chen, W., Chen, Y., & Liu, D. (2003). Adaptive Kalman filtering for vehicle navigation. Journal of Global Positioning System, 2(1), 42–47.
Kao, J., Flicker, D., Henninger, R., Ghil, M., & Ide, K. (2003). Using extended Kalman filter for data assimilation and uncertainty quantification in shock-wave dynamics. Los Alamitos, CA: IEEE Computer Society Press. 3-4942.
Kim, K. H., & Lee, J. G. (2006). Adaptive two-stage EKF for INS-GPS loosely coupled system with unknown fault bias. Journal of Global Positioning Systems, 5(1–2), 62–69.
Lee, K. S. (2011). Modeling on the cyclic operation of standing column wells under regional groundwater flow. Journal of Hydrodynamics, 23(3), 295–301.
Lin, L., Yang, J., Zhang, B., & Zhu, Y. (2010). A simplified numerical model of 3-D groundwater and solute transport at large scale area. Journal of Hydrodynamics, 22(3), 319–328.
Park, E. E., & Zhan, H. H. (2001). Analytical solutions of contaminant transport from finite one, two, and three dimensional sources in a finite thickness aquifer. Journal of Contaminant Hydrology, 53(1), 41–61.
Saad, G. A. (2007). Stochastic data assimilation with application to multi-phase flow and health monitoring problems. Doctoral dissertation. Los Angeles, CA: University of Southern California.
Tam, E. K. L., & Beyer, P. H. (2002). Remediation of contaminated lands, a decision methodology for site owners. Journal of Environmental Management, 64, 387–400.
Todd, A., Steve, B., Clint, D., Fredrik, S., Chong, W., & Mary, W. (1996). Computational methods for multiphase flow and reactive transport problems arising in subsurface contaminant remediation. Journal of Computational and Applied Mathematics, 74, 19–32.
Welch, G., & Bishop, G. (2006). An introduction to the Kalman filter. Technical Report No. TR95- 041. University of North Carolina, Department of Computer Science. Retrieved July 20, 2012, from http://www.cs.unc.edu/~welch/media/pdf/Kalman_intro.pdf.
Zheng, C., & Bennett, G. D. (1995). Applied contaminant transport modeling, theory and practice. New York: Van Nostrand-Reinhold.
Zou, S., & Parr, A. (1995). Optimal estimation of two-dimensional contaminant transport. Groundwater, 33(2), 319–325.
Acknowledgement
This work was sponsored by the Department of Energy Samuel Massie Chair of Excellence program under grant number DE-NA0000718.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chang, SY., Addai, E.B. (2016). Application of Adaptive Extended Kalman Filtering Scheme to Improve the Efficiency of a Groundwater Contaminant Transport Model. In: Uzochukwu, G., Schimmel, K., Kabadi, V., Chang, SY., Pinder, T., Ibrahim, S. (eds) Proceedings of the 2013 National Conference on Advances in Environmental Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-19923-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-19923-8_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19922-1
Online ISBN: 978-3-319-19923-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)