Use of GA to Determine Areas of Accretionto Semiconfined Aquifer
Publication: Journal of Hydraulic Engineering
Volume 127, Issue 9
Abstract
The goal of any groundwater inverse problem is to identify the distribution of an input function or certain other variables describing the unique flow dynamics of an aquifer system. A genetic algorithm (GA) combined with a numerical modeling technique is useful in determining both the spatial distribution and the flux represented by the accretion component of the groundwater flow equation. The GA technique was compared to a modified Gauss-Newton iterative technique. Binary and hexadecimal representations provided mapping of parameters from genetic operators to the numerical model. The technique used the patterns that developed in the string representations to determine probability regions. Two synthetic test cases were used to test the effectiveness of the technique. The stability of the technique was evaluated by introducing random error into the observation data. The technique was capable of locating the accretion area and tended to converge to a flux most representative of the flux entering the aquifer. However, the technique was susceptible to typical problems affecting the inverse problem, such as nonuniqueness.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Abbaspour, K. C., van Genuchter, M. T., Schulin, R., and Schläppi, E. ( 1997). “A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters.” Water Resour. Res., 33(8), 1879–1892.
2.
Ahlfeld, D. P., and Sawyer, C. S. ( 1990). “Well location in capture zone design using simulation and optimization techniques.” Ground Water, 28(4), 507–512.
3.
Bear, J. ( 1972). Dynamics of fluids in porous media, Dover, New York.
4.
Camp, C. V., Pezeshk, S., and Cao, G. (1998). “Optimized design of two-dimensional structures using genetic algorithm.”J. Struct. Engrg., ASCE, 124(5), 551–559.
5.
Carrera, J., and Neuman, S. P. ( 1986a). “Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information.” Water Resour. Res., 22(2), 199–210.
6.
Carrera, J., and Neuman, S. P. ( 1986b). “Estimation of aquifer parameters under transient and steady state conditions: 2. Uniqueness, stability and solution algorithms.” Water Resour. Res., 22(2), 211–227.
7.
Carroll, D. ( 1997). “D. L. Carroll's FORTRAN Genetic Algorithm Driver.” 〈http://www.uiuc.edu/∼carroll/ga.html〉 (Jan. 6, 1997).
8.
Cooley, R. L. ( 1977). “A method of estimating parameters and assessing reliability for models of steady state ground water flow 1. Theory and numerical properties.” Water Resour. Res., 13(2), 318–324.
9.
Dagan, G. ( 1985). “Stochastic modeling of groundwater flow by unconditional and conditional probabilities: The inverse problem.” Water Resour. Res., 21(1), 65–72.
10.
Dagan, G., and Rubin, Y. ( 1988). “Stochastic identification of recharge, transmissivity, and storativity in aquifer transient flow: A quasi-steady approach.” Water Resour. Res., 24(10), 1698–1710.
11.
El Harrouni, K., Ouazar, D., Walters, G. A., and Cheng, A. H.-D. ( 1996). “Groundwater optimization and parameter estimation by genetic algorithm and dual reciprocity boundary element method.” Engrg. Anal. with Boundary Elements, 18(4), 287–296.
12.
Goldberg, D. E. ( 1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
13.
Halhal, D., Walters, G. A., Ouazar, D., and Savic, D. A. (1997). “Water Network rehabilitation with structured messy genetic algorithm.”J. Water Resour. Plng. and Mgmt., ASCE, 123(3), 137–146.
14.
Hantush, M. H., and Mariño, M. A. ( 1994). “Two-dimensional stochastic analysis and optimal estimation in aquifers: Random recharge.” Water Resour. Res., 30(2), 559–569.
15.
Hantush, M. H., and Mariño, M. A. (1997a). “Estimation of spatially variable aquifer hydraulic properties using Kalman filtering.”J. Hydr. Engrg., ASCE, 123(11), 1027–1035.
16.
Hantush, M. H., and Mariño, M. A. (1997b). “Stochastic solution to inverse problem in ground water.”J. Hydr. Engrg., ASCE, 123(12), 1139–1146.
17.
Holland, J. H. ( 1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Mich.
18.
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.
19.
Loaiciga, H. A., and Mariño, M. A. ( 1987). “The inverse problem for confined aquifer flow: Identification and estimation with extensions.” Water Resour. Res., 23(1), 92–104.
20.
Mckinney, D. C., and Lin, M.-D. ( 1994). “Genetic algorithm solution of groundwater management models.” Water. Resour. Res., 30(6), 1897–1906.
21.
Neuman, S. P. ( 1973). “Calibration of distributed parameter groundwater flow models viewed as a multiple-objective decision process under uncertainty.” Water Resour. Res., 9(4), 1006–1021.
22.
Ouazar, D., and Cheng, A. H.-D. ( 1999). “Application of genetic algorithms in water resources.” Chapter 7, Groundwater pollution control, K. L. Katsifarakis, ed., WIT Press, 293–316.
23.
Ritzel, B. J., Eheart, J. W., and Ranjithan, S. ( 1994). “Using genetic algorithms to solve a multiple objective groundwater pollution containment problem.” Water Resour. Res., 30(5), 1589–1603.
24.
Rubin, Y., and Dagan, G. ( 1987). “Stochastic identification of transmissivity and effective recharge in steady groundwater flow 1. Theory.” Water Resour. Res., 23(7), 1185–1192.
25.
Sun, N. Z. ( 1994a). Inverse problems in groundwater modeling, Norwell: Kluwer, Boston.
26.
Sun, N. Z. ( 1994b). Inverse problems in groundwater modeling, 〈http://www.seas.ucla.edu/∼nezheng/book2 _frame.html〉 (May 2, 2000).
27.
Sun, N. Z., Jeng, M., and Yeh, W. W. ( 1995). “A proposed geological parameterization method for parameter identification in three-dimensional groundwater modeling.” Water Resour. Res., 31(1), 89–102.
28.
Sun, N. Z., and Yeh, W. W. ( 1985). “Identification of parameter structure in groundwater inverse problem.” Water Resour. Res., 21(6), 869–883.
29.
Wang, W., and Ahlfeld, D. P. ( 1986). “Optimal groundwater remediation with well location as a decision variable: Model development.” Water Resour. Res., 30(5), 1605–1618.
30.
Yeh, W. W. ( 1986). “Review of parameter identification procedures in groundwater hydrology: The inverse problem.” Water Resour. Res., 22(2), 95–108.
Information & Authors
Information
Published In
History
Received: Apr 14, 1999
Published online: Sep 1, 2001
Published in print: Sep 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.