Environmental Monitoring and Assessment

, Volume 169, Issue 1–4, pp 133–142 | Cite as

Using a hybrid approach to optimize experimental network design for aquifer parameter identification

  • Liang-Cheng Chang
  • Hone-Jay Chu
  • Yu-Pin Lin
  • Yu-Wen Chen


This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design.


Groundwater Experimental design Genetic algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Andricevic, R. (1990). A real-time approach to management and monitoring of groundwater hydraulic. Water Resources Research, 26(11), 2747–2755.CrossRefGoogle Scholar
  2. Andricevic, R. (1993). Coupled withdrawal and sampling designs for groundwater supply models. Water Resources Research, 29(1), 5–16.CrossRefGoogle Scholar
  3. Carrera, J., & Neuman, S. (1986). Estimation of aquifer parameters under transient and steady state conditions: Maximum likelihood method incorporating prior information. Water Resources Research, 22–2, 199–210.CrossRefGoogle Scholar
  4. Carrera, J., Alcolea, A., Medina, A., Hidalgo, J., & Slooten, L. J. (2005). Inverse problem in hydrogeology. Hydrogeology Journal, 13, 206–222.CrossRefGoogle Scholar
  5. Chang, L. F., Sun, N. Z., & Yeh, W. W.-G. (2005). Optimal observation network design for parameter structure identification in groundwater modeling. Water Resources Research, 41(3), W03002.CrossRefGoogle Scholar
  6. Chang, L. C., Chu, H. J., & Hsiao, C. T. (2007). Optimal planning of a dynamic pump-treat-inject groundwater remediation system. Journal of Hydrology, 342(3–4), 295–304.CrossRefGoogle Scholar
  7. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.Google Scholar
  8. Harbaugh, A. W., Banta, E. B., Hill, M. C., & McDonald, G. (2000). MODFLOW-2000, the U.S. Geological Survey Modular Ground-Water Model—user guide to modularization concepts and the ground-water flow process. US Geological Survey.Google Scholar
  9. Heidari, M., & Ranjithan, S. R. (1998). A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers. Journal of the American Water Resources Association, 34(4), 909–920.CrossRefGoogle Scholar
  10. Hilton, A. B. C., & Culver, T. B. (2000). Constraint handling for genetic algorithms in optimal remediation design. Journal of Water Resources Planning and Management, 126(3), 128–137.CrossRefGoogle Scholar
  11. Hsu, N. S., & Yeh, W. W.-G. (1989). Optimum experimental design for parameter identification in groundwater hydrology. Water Resources Research, 25(5), 1025–1040.CrossRefGoogle Scholar
  12. McDonald, M. G., & Harbaugh, A. W. (1984). A modular three-dimensional finite difference ground-water flow model. Reston: US Geological Survey.Google Scholar
  13. McKinney, D. C., & Lin, M. D. (1994). Genetic algorithm solution of groundwater management models. Water Resources Research, 30(6), 1897–1906.CrossRefGoogle Scholar
  14. Nishikawa, T., & Yeh, W. W.-G. (1989). Optimal pumping test design for the parameter identification of groundwater system. Water Resources Research, 25(7), 1737–1747.CrossRefGoogle Scholar
  15. Poeter, E. P., & Hill, M. C. (1998). Documentation of UCODE, a computer code for universal inverse modeling. U.S. Geological Survey Open-File Report 98-4080.Google Scholar
  16. Sciortino, A., Harmon, T. -C., & Yeh, W. W.-G. (2002). Experimental design and model parameter estimation for location a dissolving dense nonaqueous phase liquid pool in groundwater. Water Resources Research, 38(5), 1057.CrossRefGoogle Scholar
  17. Sidiropoulos, E., & Tolikas, P. (2004). Well location and constraint handling in groundwater pumping cost minimization via genetic algorithm. Water, Air, and Soil Pollution Focus, 4, 227–239.CrossRefGoogle Scholar
  18. Silvey, S. D. (1980). Optimal design—an introduction to the theory for parameter estimation. New York: Chapman and Hall.Google Scholar
  19. Tsai, F. T. C., & Yeh, W. W. G. (2004). Characterization and identification of aquifer heterogeneity with generalized parameterization. Water Resources Research, 40(10), W10102.CrossRefGoogle Scholar
  20. Tsai, F. T. C., Sun, N. Z., & Yeh, W. W. G. (2003). A combinatorial optimization scheme for parameter structure identification in ground water modeling. Ground Water, 41(2), 156–169.CrossRefGoogle Scholar
  21. Tung, C. P., & Chou, C. A. (2004). Pattern classification using tabu search to identify the spatial distribution of groundwater pumping. Hydrogeology Journal, 12, 488–496.CrossRefGoogle Scholar
  22. Wang, M., & Zheng, C. (1998). Groundwater management optimization using genetic algorithms and simulated annealing: Formulation and comparison. Journal of the American Water Resources Association, 34(3), 519–530.CrossRefGoogle Scholar
  23. Yeh, W. W.-G. (1986). Review of parameter identification procedures in ground-water hydrology—the inverse problem. Water Resources Research, 22(2), 95–108.CrossRefGoogle Scholar
  24. Yeh, W. W.-G., & Yoon, Y. S. (1981). Parameter identification with optimal dimension in parameterization. Water Resources Research, 17(3), 664–672.CrossRefGoogle Scholar
  25. Zheng, C., & Wang, P. (1996). Parameter structure identification using tabu search and simulated annealing. Advances in Water Resources, 19(4), 215–224.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Liang-Cheng Chang
    • 1
  • Hone-Jay Chu
    • 2
  • Yu-Pin Lin
    • 2
  • Yu-Wen Chen
    • 1
  1. 1.Department of Civil EngineeringNational Chiao Tung UniversityHsinchuTaiwan, Republic of China
  2. 2.Department of Bioenvironmental Systems EngineeringNational Taiwan UniversityTaipeiTaiwan, Republic of China

Personalised recommendations