Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 3
Abstract
Determination of groundwater contaminant source characteristics such as release histories of unknown groundwater pollutant sources from concentration observation data is an inverse problem. Often solution to this inverse problem is nonunique, and it is an ill-posed problem. A linked simulation-optimization approach can be used to solve this problem efficiently. However, this approach is computationally intensive, and the results obtained tend to be highly susceptible to errors in the measured data and estimated hydrogeological parameters. Apart from this, accuracy of the solutions is highly dependent on the choice of monitoring locations. An adaptive simulated annealing (ASA)-based solution algorithm is shown to be computationally efficient for optimal identification of the source characteristics in terms of execution time and accuracy. This computational efficiency appears to prevail even with moderate levels of errors in estimated parameters and concentration measurement errors. Also, the contaminant concentration monitoring locations are shown to be critical in the efficient characterization of the unknown contaminant sources. Optimal identification results for different monitoring networks are presented to demonstrate the relevance of a network suitable for efficient source identification.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aral, M., Guan, J., and Maslia, M. (2001). “Identification of contaminant source location and release history in aquifers.” J. Hydrol. Eng., 6(3), 225–234.
Atmadja, J., and Bagtzoglou, A. (2001). “State of the art report on mathematical methods for groundwater pollution source identification.” Environ. Forensics, 2(3), 205–214.
Bagtzoglou, A., and Atmadja, J. (2005). “Mathematical methods for hydrologic inversion: The case of pollution source identification.” Water pollution, T. Kassim, ed., Vol. 3 of Handbook of environmental chemistry, Springer, Berlin, 65–96.
Bagtzoglou, A. C., Dougherty, D. E., and Tompson, A. F. B. (1992). “Application of particle methods to reliable identification of groundwater pollution sources.” Water Resour. Manage., 6(1), 15–23.
Bagtzoglou, A. C., Tompson, A. F. B., and Dougherty, D. E. (1991). “Probabilistic simulation for reliable solute source identification in heterogeneous porous media.” Water resources engineering risk assessment, J. Ganoulis, ed., Springer, Heidelberg, 189–201.
Chadalavada, S., Datta, B., and Naidu, R. (2011). “Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site.” Environ. Monit. Assess., 173(1–4), 929–940.
Cressie, N. (1988). “Spatial prediction and ordinary kriging.” Math. Geol., 20(4), 405–421.
Datta, B. (2002). “Discussion of “Identification of contaminant source location and release history in aquifers’ by Mustafa M. Aral, Jiabao Guan, and Morris L. Maslia.” J. Hydrol. Eng., 7(5), 399–400.
Datta, B., Chakrabarty, D., and Dhar, A. (2009a). “Optimal dynamic monitoring network design and identification of unknown groundwater pollution sources.” Water Resour. Manage., 23(10), 2031–2049.
Datta, B., Chakrabarty, D., and Dhar, A. (2009b). “Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters.” J. Hydrol., 376(1–2), 48–57.
Datta, B., Chakrabarty, D., and Dhar, A. (2011). “Identification of unknown groundwater pollution sources using classical optimization with linked simulation.” J. Hydro-Environ. Res., 5(1), 25–36.
Dhar, A., and Datta, B. (2010). “Logic-based design of groundwater monitoring network for redundancy reduction.” J. Water Resour. Plann. Manage., 136(1), 88–94.
Freeze, R. A. (1975). “A stochastic-conceptual analysis of one-dimensional groundwater flow in nonuniform homogeneous media.” Water Resour. Res., 11(5), 725–741.
Gelhar, L. W. (1993). Stochastic subsurface hydrology, Prentice-Hall, Englewood Cliffs, NJ.
Gorelick, S., Evans, B., and Remson, I. (1983). “Identifying sources of groundwater pollution: An optimization approach.” Water Resour. Res., 19(3), 779–790.
Hilton, A., and Culver, T. (2005). “Groundwater remediation design under uncertainty using genetic algorithms.” J. Water Resour. Plann. Manage., 131(1), 25–34.
Holland, J. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 31.
Ingber, L. (1989). “Very fast simulated re-annealing.” Math. Comput. Modell., 12(8), 967–973.
Ingber, L. (1993). “Adaptive simulated annealing (asa).” Global optimization C-code, Caltech Alumni Association, Pasadena, CA.
Ingber, L. (1996). “Adaptive simulated annealing (asa): Lessons learned.” Control Cybern., 25, 33–54.
Ingber, L., and Rosen, B. (1992). “Genetic algorithms and very fast simulated reannealing: A comparison.” Math. Comput. Modell., 16(11), 87–100.
Javandel, I., Doughty, C., and Tsang, C. (1984). Groundwater transport: Handbook of mathematical models, American Geophysical Union, Washington, DC; Lawrence Berkeley Laboratory, Berkeley, CA.
Jim Yeh, T.-C. (1992). “Stochastic modelling of groundwater flow and solute transport in aquifers.” Hydrol. Processes, 6(4), 369–395.
Keidser, A., and Rosbjerg, D. (1991). “A comparison of 4 inverse approaches to groundwater-flow and transport parameter-identification.” Water Resour. Res., 27(9), 2219–2232.
Kirkpatrick, S. (1984). “Optimization by simulated annealing—quantitative studies.” J. Stat. Phys., 34(5–6), 975–986.
Mahar, P., and Datta, B. (1997). “Optimal monitoring network and ground-water-pollution source identification.” J. Water Resour. Plann. Manage., 123(4), 199–207.
Mahar, P. S., and Datta, B. (2000). “Identification of pollution sources in transient groundwater systems.” Water Resour. Manage., 14(3), 209–227.
Mahar, P. S., and Datta, B. (2001). “Optimal identification of ground-water pollution sources and parameter estimation.” J. Water Resour. Plann. Manage., 127(1), 20–29.
Mahinthakumar, G., and Sayeed, M. (2005). “Hybrid genetic algorithmlocal search methods for solving groundwater source identification inverse problems.” J. Water Resour. Plann. Manage., 131(1), 45–57.
Michalak, A. M., and Kitanidis, P. K. (2004). “Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling.” Water Resour. Res., 40(8), W08302.
Pebesma, E. J., and Heuvelink, G. B. M. (1999). “Latin hypercube sampling of gaussian random fields.” Technometrics, 41(4), 303–312.
Singh, R. M., and Datta, B. (2004). “Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network.” Environ. Forensics, 5(3), 143–153.
Singh, R., and Datta, B. (2006). “Identification of groundwater pollution sources using ga-based linked simulation optimization model.” J. Hydrol. Eng., 11(2), 101–109.
Singh, R., and Datta, B. (2007). “Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data.” Water Resour. Manage., 21(3), 557–572.
Singh, R., Datta, B., and Jain, A. (2004). “Identification of unknown groundwater pollution sources using artificial neural networks.” J. Water Resour. Plann. Manage., 130(6), 506–514.
Sun, N.-Z. (1994). Inverse problems in groundwater modeling, Kluwer, Dordrecht, 12–37.
Sun, A. Y., Painter, S. L., and Wittmeyer, G. W. (2006a). “A constrained robust least squares approach for contaminant release history identification.” Water Resour. Res., 42(4), W04414.
Sun, A. Y., Painter, S. L., and Wittmeyer, G. W. (2006b). “A robust approach for iterative contaminant source location and release history recovery.” J. Contam. Hydrol., 88(3–4), 181–196.
Zheng, C., Wang, P. P., Zheng, C., and Wang, P. P. (1999). Mt3dms: A modular three-dimensional multi-species transport model for simulation of advection, dispersion, and chemical reactions of contaminants in ground-water systems. Documentation and user’s guide, U.S. Army Engineer Research and Development.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Mar 22, 2011
Accepted: Mar 23, 2012
Published online: Mar 26, 2012
Published in print: Mar 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.