Water Resources Management

, Volume 13, Issue 3, pp 153–170

On Solving Aquifer Management Problems with Simulated Annealing Algorithms

  • Maria da Conceição Cunha
Article

Abstract

Aquifer systems play an essential role in meeting the ever increasing use of water for different purposes. Proper design and management of such systems should therefore be a very important matter of concern, not only to ensure that water will be available in adequate quantity (and quality) to satisfy demands but also to guarantee that this would be done in an optimal manner. This paper presents a model serving to define which water supply structures (especially pumping equipment and pipes) should be installed in order to minimize the sum of set-up costs and operation costs while satisfying demands, using a heuristic approach based on simulated annealing. Annealing algorithms are random local search optimization algorithms that allow, at least in theory and in probability, the determination of a global optimum of a (possibly constrained) function.

Aquifer management infrastructure location least-cost design and operation optimization simulated annealing 

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References

  1. Aarts, E. and Korts, J.: 1989, Simulated Annealing and Boltzman Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley and Sons Ltd., England.Google Scholar
  2. Aarts, E. H. L. and Van Laarhoven, P. J. M.: 1985, Statistical cooling: a general approach to combinatorial optimization problems, Philips J. Res. 40(4), 193–226.Google Scholar
  3. Ahlfeld, D. P.: 1986, Designing contaminated groundwater remediation systems using numerical simulation and nonlinear optimization, PhD Dissertation, Dept. of Civ. Engng., Princeton Univ. Princeton, N.J., U.S.A.Google Scholar
  4. Cerny, V.: 1985, A Thermodynamical approach to the travelling salesman problem, J. Optimization Theory and Applications 45, 41–51.Google Scholar
  5. Cunha, M. C. M. O., Hubert, P., Tyteca, D.: 1993, Optimal management of a groundwater system for seasonally varying agricultural production, Water Resour. Res. 29(7), 2415–2425.Google Scholar
  6. Dougherty, D. E., and Marryott, R. A.: 1991, Optimal groundwater management, 1. Simulated annealing, Water Resour. Res. 27(10), 2493–2508.Google Scholar
  7. Eglese, R. W.: 1990, Simulated annealing: a tool for operational research, European J. Operational Res. 46, 271–281.Google Scholar
  8. Eheart, J. W., Morgan, D. R., and Ranjithan, S.: 1991, Methods for designing hydraulic aquifer remediation techniques, In Proc. ASCE Water Resources Planning and Management Speciality Conference, American Society of Civil Engineers, New York, pp. 852–858.Google Scholar
  9. Gorelick, S. M.: 1983, A review of distributed parameter groundwater management modeling methods, Water Resour. Res. 19(2), 305–319.Google Scholar
  10. Gorelick, S. M., Freeze, R. A., Donohue, D. and Keeley, J. F.: 1993, Groundwater Contamination: Optimal Capture and Containment, Lewis Publishers, Chelsea, England.Google Scholar
  11. 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.Google Scholar
  12. Huang, M. D., Romeo, F. and Sangiovanni-Vincentelli, A.: 1986, An efficient general cooling schedule for simulated annealing, IEEE Trans. Comput. Aided Design, CAD5(1), 381–384.Google Scholar
  13. Johnson, D., Aragon, C., McGeoch, L. and Schevon, C.: 1989, Optimization by simulated annealing: an experimental evaluation; part I, Graph partitioning, Operation Research 37, 865–892.Google Scholar
  14. Johnson, V. M., Rogers, L. L. and Dowla, F. U.: 1992, Application of artificial neural network technology to the optimization of field-scale pump-and-treat remediation (abstract), EOS Trans. AGU, Fall Meeting Suppl. 73(43), 234.Google Scholar
  15. Kirkpatrick, S.: 1984, Optimization by simulated annealing: quantitative studies, Stat. Phys. 34(5/6), 975–986.Google Scholar
  16. Kirkpatrick, S., Gelatt C. and Vecchi, M.: 1983, Optimization by Simulated Annealing, Science 220 (4598), 671–680.Google Scholar
  17. Lee, Y-M. and Ellis, J. H.: 1996, Comparison of algorithms for nonlinear integer optimization: application to monitoring network design, J. Envir. Engng. 122(6), 524–531.Google Scholar
  18. Maddock III, T.: 1972, Algebric technological function from a simulation model, Water Resour. Res. 8(1), 129–134.Google Scholar
  19. Marryott, R. A., Dougherty, D. E., Stollar R. L.: 1993, Optimal groundwater management, 2, Application of simulated annealing to a field-scale contaminated site, Water Resour. Res. 29(4), 847–860.Google Scholar
  20. McKinney, D. C. and Lin, M.-D.: 1992, Genetic algorithms in groundwater flow optimization (abstract), Eos Trans. AGU., Fall Meeting Suppl. 73(43), 229.Google Scholar
  21. McKinney, D. C. and Lin, M.-D.: 1993, Groundwater optimization using genetic algorithms, paper presented at Siam Conference on Computational Issues in the Geosciences, Soc. for Ind. and Appl. Math., Houston, Tex., U.S.A., April 19–21.Google Scholar
  22. McKinney, D. C. and Lin, M.-D.: 1994, Genetic algorithm solution of groundwater management models, Water Resour. Res. 30(6), 1897–1906.Google Scholar
  23. Metropolis, N., Rosenbluthe A., Rosenbluth, M., Teller, A. and Teller, E.: 1953, Equations of State Calculations by Fast Computing Machines, J. Chemical Physics 21, 1087–1092.Google Scholar
  24. Meyer, P. D., Valocchi, A. J. and Eheart, J. W.: 1994, Monitoring network design to provide initial detection of groundwater contamination, Water Resour. Res. 30(6), 1897–1906.Google Scholar
  25. Ranjithan, S., Eheart, J. W. and Garrett Jr., J. H.: 1993, Neural network-based screening for groundwater reclamation under uncertainty, Water Resour. Res. 29(3), 563–574.Google Scholar
  26. Ritzel, B. J. and Eheart, J. W.: 1994, Using genetic algorithms to solve a multiple objective groundwater pollution containment problem, Water Resour. Res. 30(9), 2647–2659.Google Scholar
  27. Rizzo, D.M., Dougherty, D. E.: 1992, Characterization of porous media using hard and soft information, In T. Russell et al. <nt>(ed.)</nt>, Numerical Methods in Water Resources, Elsevier Applied Science, New York, pp. 449–455.Google Scholar
  28. Rogers, L. L. and Dowla, F. U.: 1992, Groundwater remediation optimization with artificial neural networks and the genetic algorithm (abstract), EOS Trans. AGU, Fall Meeting Suppl., 73(43), 186.Google Scholar
  29. Rogers, L. L. and Dowla, F. U.: 1994, Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling, Water Resour. Res. 30(2), 457–481.Google Scholar
  30. Wagner, B. J.: 1995, Recent advances in simulation-optimization groundwater management modeling, Rev. Geophys., Supplement, U.S. Natl. Rep. Int. Union Geod. Geophys., 1991/1994, Rev. Geophys. 33, 1021–1028.Google Scholar
  31. Willis, R. L. and Yeh, W. W-G.: 1987, Groundwater Systems Planning and Management, Prentice-Hall, Englewood Cliffs, N.J., U.S.A.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Maria da Conceição Cunha
    • 1
  1. 1.Instituto Superior de Engenharia de Coimbra, IPC IMAR- DECUniversidade de CoimbraCoimbraPortugal

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