Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique
Publication: Journal of Irrigation and Drainage Engineering
Volume 129, Issue 5
Abstract
Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity (T), hydraulic conductivity (K), storage coefficient (S), and leakance (L), for which the graphical method is widely used. In the present study, the efficacy of the genetic algorithm (GA) optimization technique is assessed in estimating aquifer parameters from the time-drawdown pumping test data. Computer codes were developed to optimize various aquifer parameters under different hydrogeologic conditions by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using 12 sets of the published and unpublished aquifer test data. The aquifer parameters were also estimated by the graphical method using AquiferTest software, and were compared with those obtained by the GA technique. The GA technique yielded significantly low values of the sum of square errors (SSE) for almost all the datasets under study. The results revealed that the GA technique is an efficient and reliable method for estimating various aquifer parameters, especially in the situation when the graphical matching is poor. Also, it was found that because of its inherent characteristics, GA avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. Furthermore, the performance evaluation of the developed GA-based computer codes showed that the fitness value (SSE) of the best point in a population reduces with increasing generation number and population size. The analysis of the sensitivity of the parameters during the performance of GA indicated that a unique set of aquifer parameters was obtained for all three aquifer systems. The GA-based computer programs with interactive windows developed in this study are user-friendly and can serve as a teaching and research tool, which could also be useful for practicing hydrologists and hydrogeologists.
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References
Aly, A. H., and Peralta, R. C.(1999). “Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems.” Water Resour. Res., 35(8), 2415–2425.
Batu, V. (1998). Aquifer hydraulics: A comprehensive guide to hydrogeologic data analysis, Wiley, New York, 113–627.
Bos, J.(1993). “Zoning in forest management: a quadratic assignment problem solved by simulated annealing.” J. Environ. Manage., 37, 127–145.
Brassington, R. (1998). Field hydrogeology, Wiley, Chichester, England, 248.
Chander, S., Kapoor, P. N., and Goyal, S. K.(1981). “Analysis of pumping test data using Marquardt algorithm.” Ground Water, 29(3), 275–278.
Chang, F. I., and Chen, L.(1998). “Real-coded genetic algorithm for rule-based flood control reservoir management.” Water Resour. Manage., 12, 185–198.
Cheng, A. H. D., Halhal, D., Naji, A., and Ouazar, D.(2000). “Pumping optimization in saltwater-intruded coastal aquifers.” Water Resour. Res., 36(8), 2155–2165.
Davis, L. (1991). A handbook of genetic algorithms, Van Nostrand, Reinhold, New York.
Deb, K. (2000). Optimization for engineering design, PHI Private Limited, New Delhi, India, 290–320.
DeJong, K. A. (1975). “An analysis of the behavior of a class of genetic adaptive systems.” PhD dissertation, Univ. of Michigan, Ann Arbor, Mich.
Dougherty, D. E., and Marryott, R. A.(1991). “Optimal groundwater management, 1, Simulated annealing.” Water Resour. Res., 27(10), 2493–2508.
Duan, Q., Sorooshian, S., and Gupta, V. K.(1994). “Optimal use of the SCE-UA global optimization method for calibrating watershed models.” J. Hydrol., 158(1994), 265–284.
Freeze, A. R., and Cherry, J. A. (1979). Groundwater, Prentice-Hall, Englewood Cliffs, N.J., 60–382.
Gentry, R. W., Camp, C. V., and Anderson, J. L.(2001). “Use of GA to determine areas of accretion to semiconfined aquifer.” J. Hydraul. Eng., 127(9), 738–746.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, New York, 1–145.
Goulter, I. C.(1992). “Systems analysis in water-distribution network design from theory to practice.” J. Water Resour. Plan. Manage., 118(3), 238–248.
Gwo, J.-P.(2001). “In search of preferential flow paths in structured porous media using a simple genetic algorithm.” Water Resour. Res., 37(6), 1589–1601.
Hantush, M. S., and Jacob, C. E.(1955). “Non-steady radial flow in an infinite leaky aquifer.” Trans., Am. Geophys. Union, 36(1), 95–100.
Hilton, A. B. C., and Culver, T. B.(2000). “Constraint handling for genetic algorithms in optimal remediation design.” J. Water Resour. Plan. Manage., 126(3), 128–137.
Holland, J. H. (1975). Adaptation in natural and artificial systems, 2nd Ed., MIT Press, Cambridge, Mass., 10–69.
Jowitt, P. W., and Xu, C.(1990). “Optimal valve control in water-distribution networks.” J. Water Resour. Plan. Manage., 116(4), 455–472.
Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H., and Muttil, N.(2001). “Genetic programming and its application in real-time runoff forecasting.” J. Am. Water Resour. Assoc.,37(2), 439–450.
Koumousis, V. K., and Georgiou, P. G.(1994). “Genetic algorithms in discrete optimization of steel truss roofs.” J. Comput. Civ. Eng., 8(3), 309–325.
Kuo, C.-H., Michel, A. N., and Gray, W. G.(1992). “Design of optimal pump-and-treat strategies for contaminated groundwater remediation using the simulated annealing algorithm.” Adv. Water Resour., 15, 95–105.
Lee, T.-C., Perina, T., and Lee, C.-Y.(2002). “Validation of aquifer parameter determination by extrapolation fitting and treating thickness as an unknown.” J. Hydrol., 265, 15–33.
Lockwood, C., and Moore, T.(1993). “Harvest scheduling with spatial constraints: a simulated annealing approach.” Can. J. Forestry Res.,23, 468–478.
Mayer, D. G., Belward, J. A., and Burrage, K.(2001). “Robust parameter settings of evolutionary algorithms for the optimization of agricultural system models.” Agric. Systems,69(2001), 199–213.
Mayer, D. G., Belward, J. A., Widell, H., and Burrage, K.(1999). “Survival of the fittest—Genetic algorithms versus evolution strategies in the optimization of systems models.” Agric. Systems,60, 113–122.
McKinney, D. C., and Lin, M. D.(1994). “Genetic algorithm solution of groundwater management models.” Water Resour. Res., 30(6), 1897–1906.
Meier, R. W., and Barkdoll, B. D.(2000). “Sampling design for network model calibration using genetic algorithms.” J. Water Resour. Plan. Manage., 126(4), 245–250.
Mohan, S.(1997). “Parameter estimation of nonlinear Muskingum models using genetic algorithm.” J. Hydraul. Eng., 123(2), 137–142.
Morshed, J., and Kaluarachchi, J. J.(2000). “Enhancements to genetic algorithm for optimal ground-water management.” J. Hydrol. Eng., 5(1), 67–73.
Prasad, K. L., and Rastogi, A. K.(2001). “Estimating net aquifer recharge and zonal hydraulic conductivity values for Mahi Right Bank Canal project area, India by genetic algorithm.” J. Hydrol., 243, 149–161.
Ranjithan, S., Eheart, J. W., and Garrett, J. H.(1993). “Neural network-based screening for groundwater reclamation under uncertainty.” Water Resour. Res., 29(3), 563–574.
Reed, P., Minsker, B., and Goldberg, D. E.(2000a). “Designing a competent simple genetic algorithm for search and optimization.” Water Resour. Res., 36(12), 3757–3761.
Reed, P., Minisker, B. S., and Valocchi, A. J.(2000b). “Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation.” Water Resour. Res., 36(12), 3731–3741.
Ritzel, B. J., and Eheart, W.(1994). “Using genetic algorithms to solve a multiple objective groundwater pollution containment problem.” Water Resour. Res., 30(5), 1589–1603.
Roscoe Moss Company. (1990). Handbook of groundwater development, Wiley, New York, 276–312.
Samuel, M. P. (2002). “Determination of aquifer and well parameters using genetic algorithm.” MTech thesis, Indian Institute of Technology, Kharagpur, India.
Savic, D. A., and Walters, G. A.(1995a). “Integration of a model for hydraulic analysis of water distribution networks with an evolution program for pressure regulation.” Microcomput. Civ. Eng., 10(3), 219–229.
Savic, D. A., and Walters, G. A.(1995b). “An evolution program for optimal pressure regulation in water distribution networks.” Eng. Optimiz., 24(3), 197–219.
Savic, D. A., Walters, G. A., and Davidson, J. W.(1999). “A genetic programming approach to rainfall-runoff modeling.” Water Resour. Manage., 13, 219–231.
Simpson, A. R., Dandy, G. C., and Murphy, L. J.(1994). “Genetic algorithms compared to other techniques for pipe optimization.” J. Water Resour. Plan. Manage., 120(4), 423–443.
Smalley, J. B., Minsker, B. S., and Goldberg, D. E.(2000). “Risk-based in situ bioremediation design using a noisy genetic algorithm.” Water Resour. Res., 36(10), 3043–3052.
Sun, N.-Z. (1994). Inverse problems in groundwater modeling, Kluwer Academic, Dordrecht, the Netherlands.
Theis, C. V.(1935). “The relation between the lowering of the piezometric surface and the rate and duration of discharge of well using groundwater storage.” Trans., Am. Geophysi. Union, 16, 519–524.
Todd, D. K. (1980). Groundwater hydrology, Wiley, New York, 535.
Tseng, P. H., and Lee, T. C.(1998). “Numerical evaluation of exponential integral: Theis well function approximation.” J. Hydrol., 205, 38–51.
Wang, Q. J.(1991). “The genetic algorithm and its application to calibrating conceptual rainfall-runoff models.” Water Resour. Res., 27(9), 2467–2471.
Wardlaw, R., and Sharif, M.(1999). “Evaluation of genetic algorithms for optimal reservoir system operation.” J. Water Resour. Plan. Manage., 125(1), 25–33.
Willis, R., and Yeh, W. W.-G. (1987). Groundwater system planning and management, Prentice-Hall, Englewood Cliffs, N.J.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 129 • Issue 5 • October 2003
Pages: 348 - 359
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Mar 27, 2002
Accepted: Apr 25, 2003
Published online: Sep 15, 2003
Published in print: Oct 2003
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