Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 369369, 13 pages
http://dx.doi.org/10.1155/2014/369369
Research Article

Applying Hybrid Heuristic Approach to Identify Contaminant Source Information in Transient Groundwater Flow Systems

Institute of Environmental Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan

Received 9 April 2014; Accepted 25 July 2014; Published 18 August 2014

Academic Editor: Tzu-Yang Yu

Copyright © 2014 Hund-Der Yeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Atmadja and A. C. Bagtzoglou, “State of the art report on mathematical methods for groundwater pollution source identification,” Environmental Forensics, vol. 2, no. 3, pp. 205–214, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. P. S. Mahar and B. Datta, “Optimal identification of ground-water pollution sources and parameter estimation,” Journal of Water Resources Planning and Management, vol. 127, no. 1, pp. 20–29, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Datta, “Discussion of “Identification of contaminant source location and release history in aquifers” by Mustafa M. Aral, Jiabao Guan, and Morris L. Maslia,” Journal of Hydrologic Engineering, vol. 7, no. 5, pp. 399–400, 2002. View at Google Scholar
  4. R. M. Singh and B. Datta, “Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data,” Water Resources Management, vol. 21, no. 3, pp. 557–572, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Li and X.-Z. Mao, “Global multiquadric collocation method for groundwater contaminant source identification,” Environmental Modelling and Software, vol. 26, no. 12, pp. 1611–1621, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Jha and B. Datta, “Three-dimensional groundwater contamination source identification using adaptive simulated annealing,” Journal of Hydrologic Engineering, vol. 18, no. 3, pp. 307–317, 2013. View at Google Scholar
  7. A. Y. Sun, “A robust geostatistical approach to contaminant source identification,” Water Resources Research, vol. 43, no. 2, Article ID W02418, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. S. M. Gorelick, B. Evans, and I. Remson, “Identifying sources of groundwater pollution: an optimization approach,” Water Resources Research, vol. 19, no. 3, pp. 779–790, 1983. View at Publisher · View at Google Scholar · View at Scopus
  9. J. C. Hwang and R. M. Koerner, “Groundwater pollution source identification from limited monitoring well data. Part 1. Theory and feasibility,” Journal of Hazardous Materials, vol. 8, no. 2, pp. 105–119, 1983. View at Publisher · View at Google Scholar · View at Scopus
  10. National Research Council, Groundwater Models—Scientific and Segulatory Applications, National Academy Press, Washington, DC, USA, 1990.
  11. A. C. Bagtzoglou, D. E. Dougherty, and A. F. B. Tompson, “Application of particle methods to reliable identification of groundwater pollution sources,” Water Resources Management, vol. 6, no. 1, pp. 15–23, 1992. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Sciortino, T. C. Harmon, and W. W.-G. Yeh, “Inverse modeling for locating dense nonaqueous pools in groundwater under steady flow conditions,” Water Resources Research, vol. 36, no. 7, pp. 1723–1735, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Mahinthakumar and M. Sayeed, “Hybrid genetic algorithm—local search methods for solving groundwater source identification inverse problems,” Journal of Water Resources Planning and Management, vol. 131, no. 1, pp. 45–57, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Milnes and P. Perrochet, “Simultaneous identification of a single pollution point-source location and contamination time under known flow field conditions,” Advances in Water Resources, vol. 30, no. 12, pp. 2439–2446, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. H. D. Yeh, T. H. Chang, and Y. C. Lin, “Groundwater contaminant source identification by a hybrid heuristic approach,” Water Resources Research, vol. 43, no. 9, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Datta, D. Chakrabarty, and A. Dhar, “Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters,” Journal of Hydrology, vol. 376, no. 1-2, pp. 48–57, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. M. T. Ayvaz, “A linked simulation-optimization model for solving the unknown groundwater pollution source identification problems,” Journal of Contaminant Hydrology, vol. 117, no. 1–4, pp. 46–59, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. B. J. Wagner, “Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling,” Journal of Hydrology, vol. 135, no. 1–4, pp. 275–303, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. T. H. Skaggs and Z. J. Kabala, “Recovering the release history of a groundwater contaminant,” Water Resources Research, vol. 30, no. 1, pp. 71–79, 1994. View at Publisher · View at Google Scholar · View at Scopus
  20. T. H. Skaggs and Z. J. Kabala, “Recovering the history of a groundwater contaminant plume: method of quasi-reversibility,” Water Resources Research, vol. 31, no. 11, pp. 2669–2673, 1995. View at Publisher · View at Google Scholar · View at Scopus
  21. T. H. Skaggs and Z. J. Kabala, “Limitations in recovering the history of a groundwater contaminant plume,” Journal of Contaminant Hydrology, vol. 33, no. 3-4, pp. 347–359, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. A. D. Woodbury and T. J. Ulrych, “Minimum relative entropy inversion: theory and application to recovering the release history of a groundwater contaminant,” Water Resources Research, vol. 32, no. 9, pp. 2671–2681, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. M. F. Snodgrass and P. K. Kitanidis, “A geostatistical approach to contaminant source identification,” Water Resources Research, vol. 33, no. 4, pp. 537–546, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Woodbury, E. Sudicky, T. J. Ulrych, and R. Ludwig, “Three-dimensional plume source reconstruction using minimum relative entropy inversion,” Journal of Contaminant Hydrology, vol. 32, no. 1-2, pp. 131–158, 1998. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Chongxuan and W. P. Ball, “Application of inverse methods to contaminant source identification from aquitard diffusion profiles at Dover AFB, Delaware,” Water Resources Research, vol. 35, no. 7, pp. 1975–1985, 1999. View at Publisher · View at Google Scholar · View at Scopus
  26. R. M. Neupauer and J. L. Wilson, “Adjoint method for obtaining backward-in-time location and travel time probabilities of a conservative groundwater contaminant,” Water Resources Research, vol. 35, no. 11, pp. 3389–3398, 1999. View at Publisher · View at Google Scholar · View at Scopus
  27. R. M. Neupauer and J. L. Wilson, “Adjoint-derived location and travel time probabilities for a multidimensional groundwater system,” Water Resources Research, vol. 37, no. 6, pp. 1657–1668, 2001. View at Publisher · View at Google Scholar · View at Scopus
  28. R. M. Neupauer, B. Borchers, and J. L. Wilson, “Comparison of inverse methods for reconstructing the release history of a groundwater contamination source,” Water Resources Research, vol. 36, no. 9, pp. 2469–2475, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Atmadja and A. C. Bagtzoglou, “Pollution source identification in heterogeneous porous media,” Water Resources Research, vol. 37, no. 8, pp. 2113–2125, 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. A. C. Bagtzoglou and J. Atmadja, “The Marching-Jury backward beam equation and quasi-reversibility methods for hydrologic inversion: application to contaminant plume spatial distribution recovery,” Water Resources Research, vol. 39, no. 2, pp. 1038–1051, 2003. View at Google Scholar
  31. I. Butera and M. G. Tanda, “A geostatistical approach to recover the release history of groundwater pollutants,” Water Resources Research, vol. 39, article 1372, no. 12, 2003. View at Google Scholar · View at Scopus
  32. S. Shlomi and A. M. Michalak, “A geostatistical framework for incorporating transport information in estimating the distribution of a groundwater contaminant plume,” Water Resources Research, vol. 43, no. 3, Article ID W03412, 2007. View at Publisher · View at Google Scholar · View at Scopus
  33. N. M. Muhammad, K.-Y. Kim, C.-H. Huang, and S. Kim, “Groundwater contaminant boundary input flux estimation in a two-dimensional aquifer,” Journal of Industrial and Engineering Chemistry, vol. 16, no. 1, pp. 106–114, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. A. D. Koussis, K. Mazi, S. Lykoudis, and A. A. Argiriou, “Reverse flood routing with the inverted Muskingum storage routing scheme,” Natural Hazards and Earth System Science, vol. 12, no. 1, pp. 217–227, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. M. M. Aral and J. Guan, “Genetic algorithms in search of groundwater pollution sources,” Advances in Groundwater Pollution Control and Remediation, vol. 9, pp. 347–369, 1996. View at Google Scholar
  36. A. C. Bagtzoglou, “On the nonlocality of reversible-time particle tracking methods,” Environmental Forensics, vol. 4, no. 3, pp. 215–225, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. A. C. Bagtzoglou and J. Atmadja, “Mathematical methods for hydrologic inversion: the case of pollution source identification,” in Water Pollution, vol. 3 of The Handbook of Environmental Chemistry, pp. 65–96, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  38. R. M. Neupauer and R. Lin, “Identifying sources of a conservative groundwater contaminant using backward probabilities conditioned on measured concentrations,” Water Resources Research, vol. 42, no. 3, Article ID W03424, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Y. Sun, S. L. Painter, and G. W. Wittmeyer, “A constrained robust least squares approach for contaminant release history identification,” Water Resources Research, vol. 42, no. 4, Article ID W04414, 2006. View at Publisher · View at Google Scholar · View at Scopus
  40. A. Y. Sun, S. L. Painter, and G. W. Wittmeyer, “A robust approach for iterative contaminant source location and release history recovery,” Journal of Contaminant Hydrology, vol. 88, no. 3-4, pp. 181–196, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. R. Ababou, A. C. Bagtzoglou, and A. Mallet, “Anti-diffusion and source identification with the “RAW” scheme: a particle-based censored random walk,” Environmental Fluid Mechanics, vol. 10, no. 1, pp. 41–76, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. I. Butera, M. G. Tanda, and A. Zanini, “Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach,” Stochastic Environmental Research and Risk Assessment, vol. 27, no. 5, pp. 1269–1280, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. M. M. Aral, J. Guan, and M. L. Maslia, “Identification of contaminant source location and release history in aquifers,” Journal of Hydrologic Engineering, vol. 6, no. 3, pp. 225–234, 2001. View at Publisher · View at Google Scholar · View at Scopus
  44. R. M. Neupauer and J. L. Wilson, “Backward probability model using multiple observations of contamination to identify groundwater contamination sources at the Massachusetts Military Reservation,” Water Resources Research, vol. 41, no. 2, pp. 1–14, 2005. View at Publisher · View at Google Scholar · View at Scopus
  45. Y. C. Ho, R. S. Sreenivas, and P. Vakili, “Ordinal optimization of DEDS,” Discrete Event Dynamic Systems, vol. 2, no. 1, pp. 61–88, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  46. Y. Ho and M. E. Larson, “Ordinal optimization approach to rare event probability problems,” Discrete Event Dynamic Systems: Theory and Applications, vol. 5, no. 2-3, pp. 281–301, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  47. T. W. E. Lau and Y.-C. Ho, “Universal alignment probabilities and subset selection for ordinal optimization,” Journal of Optimization Theory and Applications, vol. 93, no. 3, pp. 455–489, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  48. Y.-C. Ho, “An explanation of ordinal optimization: soft computing for hard problems,” Information Sciences, vol. 113, no. 3-4, pp. 169–192, 1999. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  49. M. C. Fu, “Optimization for simulation: theory vs. practice,” INFORMS Journal on Computing, vol. 14, no. 3, pp. 192–215, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
  50. L. F. Konikow, D. J. Goode, and G. Z. Hornberger, “A three-dimensional method of characteristics solute-transport model (MOC3D),” U.S. Geological Survey Water-Resources Investigations Report 96-4267, 1996. View at Google Scholar
  51. A. W. Harbaugh, E. R. Banta, M. C. Hill, and M. G. McDonald, “MODFLOW-2000, the U.S. Geological survey modular ground-water model—user guide to modularization concepts and the ground-water flow process,” U.S. Geological Survey, Open File Rep, 2000.
  52. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes, Cambridge University Press, Cambridge, UK, 2nd edition, 1992. View at MathSciNet
  53. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of state calculations by fast computing machines,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953. View at Google Scholar · View at Scopus
  54. Y.-C. Huang and H.-D. Yeh, “The use of sensitivity analysis in on-line aquifer parameter estimation,” Journal of Hydrology, vol. 335, no. 3-4, pp. 406–418, 2007. View at Publisher · View at Google Scholar · View at Scopus
  55. H.-D. Yeh and Y.-J. Chen, “Determination of skin and aquifer parameters for a slug test with wellbore-skin effect,” Journal of Hydrology, vol. 342, no. 3-4, pp. 283–294, 2007. View at Publisher · View at Google Scholar · View at Scopus
  56. H. Yeh, Y. Lin, and Y. Huang, “Parameter identification for leaky aquifers using global optimization methods,” Hydrological Processes, vol. 21, no. 7, pp. 862–872, 2007. View at Publisher · View at Google Scholar · View at Scopus
  57. H. Yeh and Y. Lin, “Pipe network system analysis using simulated annealing,” Journal of Water Supply: Research and Technology—AQUA, vol. 57, no. 5, pp. 317–327, 2008. View at Publisher · View at Google Scholar · View at Scopus
  58. Y.-C. Lin and H.-D. Yeh, “Identifying groundwater pumping source information using optimization approach,” Hydrological Processes, vol. 22, no. 16, pp. 3010–3019, 2008. View at Google Scholar
  59. F. Glover, “Future paths for integer programming and links to artificial intelligence,” Computers & Operations Research, vol. 13, no. 5, pp. 533–549, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  60. C. Zheng and P. Wang, “Parameter structure identification using tabu search and simulated annealing,” Advances in Water Resources, vol. 19, no. 4, pp. 215–224, 1996. View at Publisher · View at Google Scholar · View at Scopus
  61. C. Tung and C. Chou, “Pattern classification using tabu search to identify the spatial distribution of groundwater pumping,” Hydrogeology Journal, vol. 12, no. 5, pp. 488–496, 2004. View at Publisher · View at Google Scholar · View at Scopus
  62. X. Guan, Y. C. Ho, and F. Lai, “An ordinal optimization based bidding strategy for electric power suppliers in the daily energy market,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 788–797, 2001. View at Publisher · View at Google Scholar · View at Scopus
  63. S. Lin, Y. Ho, and C. Lin, “An ordinal optimization theory-based algorithm for solving the optimal power flow problem with discrete control variables,” IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 276–286, 2004. View at Publisher · View at Google Scholar · View at Scopus
  64. Y. Liu, J. Chen, and M. Xie, “Distribution network planning based on the ordinal optimization theory,” Automation of Electric Power Systems, vol. 30, no. 22, pp. 21–24, 2006. View at Google Scholar · View at Scopus
  65. S.-Y. Lin and S.-C. Horng, “Application of an ordinal optimization algorithm to the wafer testing process,” IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans, vol. 36, no. 6, pp. 1229–1234, 2006. View at Publisher · View at Google Scholar · View at Scopus
  66. IMSL, Fortran Library User's Guide Gtat/Library, vol. 2, Visual Numerics, Houston, Tex, USA, 2003.