Identification of Contaminant Source Location and Release History in Aquifers
This article has a reply.
VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 6, Issue 3
Abstract
In this study, we formulate a contaminant source characterization problem as a nonlinear optimization model, in which contaminant source locations and release histories are defined as explicit unknown variables. The optimization model selected is the standard model, in which the residuals between the simulated and measured contaminant concentrations at observation sites are minimized. In the proposed formulation, simulated concentrations at the observation locations are implicitly embedded into the optimization model through the solution of ground-water flow and contaminant fate and transport simulation models. It is well known that repeated solutions of these models, which is a necessary component of the optimization process, dominate the computational cost and adversely affect the efficiency of this approach. To simplify this computationally intensive process, a new combinatorial approach, identified as the progressive genetic algorithm, is proposed for the solution of the nonlinear optimization model. Numerical experiments show that the proposed approach provides a robust tool for the solution of ground-water contaminant source identification problems.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Ahlfeld, D. P., Mulvey, J. M., Pinder, G. F., and Wood, E. F. ( 1988). “Contaminated ground-water remediation design using simulation, optimization and sensitivity theory—1. Model development.” Water Resour. Res., 24(3), 431–441.
2.
Aral, M. M. ( 1990). Ground-water modeling in multilayer aquifers—steady flow, Lewis, Boca Raton, Fla.
3.
Aral, M. M., and Guan, J. ( 1996). Genetic algorithms in search of ground-water pollution sources, advances in ground-water pollution control and remediation, M. M. Aral, ed., Vol. 9, Kluwer, Dordrecht, The Netherlands, 346–371.
4.
Aral, M. M., and Guan, J. ( 1997). “Optimal ground-water remediation system design with well locations selected as decision variables.” Tech. Rep. MESL-01-97, Multimedia Envir. Simulations Lab., School of Civ. and Envir. Engrg., Georgia Institute of Technology, Atlanta.
5.
Aral, M. M., and Guan, J. ( 1998). “Identification of ground-water contaminant sources and release histories using genetic algorithms.” Tech. Rep. MESL-01-98, Multimedia Envir. Simulations Lab., School of Civ. and Envir. Engrg., Georgia Institute of Technology, Atlanta.
6.
Datta, B., Beegle, J. E., Kavvas, M. L., and Orlob, G. T. ( 1989). “Development of an expert system embedding pattern recognition techniques for pollution source identification.” Res. Rep., University of California–Davis, Davis, Calif.
7.
Datta, B., and Peralta, R. C. ( 1986). “Expert pattern recognition for pollution source identification.” Water Forum '86, Vol. 1, M. Karamouz et al., eds., ASCE, New York, 195–202.
8.
Dougherty, D. E., and Marryott, R. A. ( 1991). “Optimal ground-water management—1. Simulated annealing.” Water Resour. Res., 27(10), 2493–2508.
9.
Goldberg, D. E. ( 1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
10.
Gorelick, S. M., Evans, B., and Remson, I. ( 1983). “Identifying sources of ground-water pollution: An optimization approach.” Water Resour. Res., 19(3), 779–790.
11.
Guan, J., and Aral, M. M. ( 1999a). “Progressive genetic algorithm for solution of optimization problems with nonlinear equality and inequality constraints.” Appl. Math. Modeling, 23, 329–343.
12.
Guan, J., and Aral, M. M. ( 1999b). “Optimal remediation with well locations and pumping rates selected as continuous decision variables.” J. Hydro., Amsterdam, 221, 20–42.
13.
Holland, J. H. ( 1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Mich.
14.
Huang, C., and Mayer, A. S. ( 1997). “Pump-and-treat optimization using well locations and pumping rates as decision variables.” Water Resour. Res., 33(5), 1001–1012.
Information & Authors
Information
Published In
History
Received: Dec 17, 1999
Published online: Jun 1, 2001
Published in print: Jun 2001
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.