Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics

Water Resources Research

Volumn 47, Issue 2, 2011, Pages

  Kollat, J B ^a   Reed, P M ^a   Maxwell, R M ^b  

^a The Pennsylvania State University   (United States)

^b Department of Geology and Geological Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE STRATEGY; DECISION MAKERS; DECISION SUPPORTS; ENSEMBLE KALMAN FILTERING; EVOLUTIONARY OPTIMIZATIONS; GROUNDWATER MONITORING; GROUNDWATER SYSTEM; MODEL PREDICTION; MONITORING COSTS; NETWORK DESIGN; PLUME TRANSPORT; SPACE AND TIME; SYSTEMATIC MODELS; TRACER EXPERIMENT; TRACER FLUXES; TRACER MASS; TRANSPORT MODELING; UNCERTAIN MEASUREMENTS; VISUAL ANALYTICS;

AQUIFERS; DECISION SUPPORT SYSTEMS; ERROR DETECTION; FORECASTING; KALMAN FILTERS; MONITORING; OPTIMIZATION; SYSTEMATIC ERRORS; VISUALIZATION;

GROUNDWATER RESOURCES;

AQUIFER POLLUTION; DECISION SUPPORT SYSTEM; ENSEMBLE FORECASTING; KALMAN FILTER; OPTIMIZATION; POLLUTANT TRANSPORT; SAMPLING; TRACER;

EID: 79951973284     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010WR009194     Document Type: Article

References (103)

1. Ammar, K., A. Khalil, M. McKee, and J. Kaluarachchi (2008), Bayesian deduction for redundancy detection in groundwater quality monitoring networks, Water Resour. Res., 44, W08412, doi:10.1029/2006WR005616.
2. Andricevic, R. (1990), Cost-effective network design for groundwater flow monitoring, Stochastic Hydrol. Hydraul., 27-41.
3. Arnold, C., and C. Dey (1986), Observing-systems simulation experiments: Past, present, and future, Bull. Am. Meteorol. Soc., 67(6), 687-695.
4. Asefa, T., M. W. Kemblowski, G. Urroz, M. McKee, and A. Khalil (2004), Support vectors-based groundwater head observation networks design, Water Resour. Res., 40, W11509, doi:10.1029/2004WR003304.
5. Asefa, T., M. Kemblowski, G. Urroz, and M. McKee (2005), Support vector machines (svms) for monitoring network design, Ground Water, 43(3), 413-422. (Pubitemid 40697666)
6. Ashby, S., and R. Falgout (1996), A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations, Nucl. Sci. Eng., 124(1), 145-159. (Pubitemid 126564896)
7. Bogárdi, I., A. Bárdossy, and L. Duckstein (1985), Multicriterion network design using geostatistics, Water Resour. Res., 21(2), 199-208.
8. Brill, E., J. Flach, L. Hopkins, and S. Ranjithan (1990), MGA: A decision support system for complex, incompletely defined problems, IEEE Trans. Syst. Man Cybernet., 20(4), 745-757. (Pubitemid 20739305)
9. Carrera, J., E. Usunoff, and F. Szidarovszky (1984), A method for optimal observation network design for groundwater management, J. Hydrol., 73(1-2), 147-163.
10. Chadalavada, S., and B. Datta (2008), Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers, Water Resour. Manage., 22(6), 651-670. (Pubitemid 351486328)
11. Cieniawski, S. E., J. W. Eheart, and S. R. Ranjithan (1995), Using genetic algorithms to solve a multiobjective groundwater monitoring problem, Water Resour. Res., 31(2), 399-409.
12. Cleveland, T., and W. Yeh (1990), Sampling network design for transport parameter identification, J. Water Resour. Plann. Manage., 116(6), 764-783.
13. Cleveland, T., and W. Yeh (1991), Optimal configuration and scheduling of ground-water tracer test, J. Water Resour. Plann. Manage., 117(1), 37-51.
14. Coello Coello, C. A., G. B. Lamont, and D. A. V. Veldhuizen (2007), Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, New York.
15. Deb, K., and H. Gupta (2006), Introducing robustness in multi-objective optimization, Evol. Comput., 14(4), 463-494.
16. Deutsch, C. V., and A. G. Journel (1998), GSLIB: Geostatistical Software Library and User's Guide, Oxford Univ. Press, New York.
17. Dhar, A., and B. Datta (2007), Multiobjective design of dynamic monitoring networks for detection of groundwater pollution, J. Water Resour. Plann. Manage., 133(4), 329-338. (Pubitemid 47009153)
18. di Pierro, F., S. Khu, and D. Savic (2007), An investigation on preference order ranking scheme for multiobjective evolutionary optimization, IEEE Trans. Evol. Comput., 11(1), 17-45. (Pubitemid 46421666)
19. Dokou, Z., and G. Pinder (2009), Optimal search strategy for the definition of a DNAPL source, J. Hydrol., 376(3-4), 542-556.
20. Drécourt, J.-P., H. Madsen, and D. Rosbjerg (2006), Bias aware Kalman filters: Comparison and improvements, Adv. Water Resour., 29(5), 707-718.
21. Drozdov, O. A., and A. A. Sepelevskij (1946), The theory of the interpolation of meteorological elements in a stochastic field and their application to questions of weather maps and network rationalization, Tr. NIU GUGMS Ser. 1, 13.
22. Eigbe, U., M. B. Beck, H. S. Wheater, and F. Hirano (1998), Kalman filtering in groundwater flow modelling: Problems and prospects, Stochastic Hydrol. Hydraul., 12(1), 15-32.
23. Eppstein, M. J., and D. E. Dougherty (1996), Simultaneous estimation of transmissivity values and zonation, Water Resour. Res., 32(11), 3321-3336.
24. Eppstein, M. J., and D. E. Dougherty (1998), Optimal 3-D traveltime tomography, Geophys., 63(3), 1053-1061.
25. Evensen, G. (1994), Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99(C5), 10,143-10,162, doi:10.1029/94JC00572.
26. Evensen, G. (2003), The ensemble kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53(4), 343-367.
27. Fiering, M. (1965), An optimization scheme for gaging, Water Resour. Res., 1(4), 463-470.
28. Fleming, P. J., R. C. Purshouse, and R. J. Lygoe (2005), Many-objective optimization: An engineering design perspective, in Evolutionary Multi-Criterion Optimization, vol. 3410, edited by C. C. Coello, A. Hernandez, and E. Zitzler, pp. 14-32, Springer, Berlin.
29. Frei, S., J. Fleckenstein, S. Kollet, and R. Maxwell (2009), Patterns and dynamics of river-aquifer exchange with variably-saturated flow using a fully-coupled model, J. Hydrol., 375(3-4), 383-393.
30. Goldberg, D. E. (2002), The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Kluwer Acad., Norwell, Mass.
31. Graham, W., and D. McLaughlin (1989a), Stochastic analysis of nonstationary subsurface solute transport 1: Unconditional moments, Water Resour. Res., 25(2), 215-232. (Pubitemid 19303633)
32. Graham, W., and D. McLaughlin (1989b), Stochastic analysis of nonstationary subsurface solute transport 2: Conditional moments, Water Resour. Res., 25(11), 2331-2355. (Pubitemid 20404078)
33. Graham, W., and D. McLaughlin (1991), A stochastic model of solute transport in groundwater: Application to the borden, ontario tracer test, Water Resour. Res., 27(6), 1345-1359.
34. Hanne, T. (1999), On the convergence of multiobjective evolutionary algorithms, Eur. J. Oper. Res., 117(3), 553-564.
35. Herrera, G. S., and G. Pinder (2005), Space-time optimization of groundwater quality sampling networks, Water Resour. Res., 41, W12407, doi:10.1029/2004WR003626.
36. Hudak, P. (2006), Heuristic for constructing minimum-well groundwater monitoring configurations at waste storage facilities, Environ. Sci. Health, 41(2), 185-193.
37. Hudak, P., and H. Loaiciga (1992), A location modeling approach for groundwater monitoring network augmentation, Water Resour. Res., 28(3), 643-649.
38. Hudak, P. F., and H. A. Loaiciga (1993), An optimization method for monitoring network design in multilayered groundwater flow systems, Water Resour. Res., 29(8), 2835-2845.
39. Hughes, J., and D. P. Lettenmaier (1981), Data requirements for kriging: Estimation and network design, Water Resour. Res., 17(6), 1641-1650.
40. James, B. R., and S. M. Gorelick (1994), When enough is enough: The worth of monitoring data in aquifer remediation design, Water Resour. Res., 30(12), 3499-3513.
41. Jones, J., and C. Woodward (2001), Newton-Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems, Adv. Water Resour., 24(7), 763-774. (Pubitemid 32593051)
42. Kasprzyk, J. R., P. M. Reed, B. R. Kirsch, and G. W. Characklis (2009), Managing population and drought risks using many-objective water portfolio planning under uncertainty, Water Resour. Res., 45, W12401, doi:10.1029/ 2009WR008121.
43. Khu, S., and H. Madsen (2005), Multiobjective calibration with Pareto preference ordering: An application to rainfall-runoff model calibration, Water Resour. Res., 41, W03004, doi:10.1029/2004WR003041.
44. Kim, K. H., and K. K. Lee (2007), Optimization of groundwater-monitoring networks for identification of the distribution of a contaminant plume, Stochastic Environ. Res. Risk Assess., 21(6), 785-794.
45. Knopman, D. S., and C. I. Voss (1988), Discrimination among one-dimensional models of solute transport, Water Resour. Res., 24(11), 1859-1876.
46. Kollat, J. B., and P. M. Reed (2006), Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design, Adv. Water Resour., 29(6), 792-807.
47. Kollat, J. B., and P. M. Reed (2007a), A framework for visually interactive decision-making and design using evolutionary multiobjective optimization (VIDEO), Environ. Modell. Software, 22(12), 1691-1704.
48. Kollat, J. B., and P. M. Reed (2007b), A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications, Adv. Water Resour., 30(3), 408-419.
49. Kollat, J., P. Reed, and D. Rizzo (2008a), Addressing model bias and uncertainty in three dimensional groundwater transport forecasts for a physical aquifer experiment, Geophys. Res. Lett., 35, L17402, doi:10.1029/2008GL035021.
50. Kollat, J. B., P. M. Reed, and J. R. Kasprzyk (2008b), A new epsilon-dominance hierarchical bayesian optimization algorithm for large multiobjective monitoring network design problems, Adv. Water Resour., 31(5), 828-845.
51. Kollet, S., and R. Maxwell (2006), Integrated surface-groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29(7), 945-958. (Pubitemid 43832572)
52. Kollet, S., and R. Maxwell (2008), Demonstrating fractal scaling of base-flow residence time distributions using a fully-coupled groundwater and land surface model, Geophys. Res. Lett., 35, L07402, doi:10.1029/2008GL033215.
53. Kollet, S., et al. (2010), Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources, Water Resour. Res., 46, W04201, doi:10.1029/2009WR008730.
54. Langbein, W. B. (1979), Overview of conference on hydrologic data networks, Water Resour. Res., 15(6), 1867-1871.
55. Laumanns, M., L. Thiele, K. Deb, and E. Zitzler (2002), Combining convergence and diversity in evolutionary multiobjective optimization, Evol. Comput., 10(3), 263-282.
56. Lettenmaier, D. P. (1979), Dimensionality problems in water quality network design, Water Resour. Res., 13(6), 1692-1700.
57. Loaiciga, H. (1989), An optimization approach for groundwater quality monitoring network design, Water Resour. Res., 25(8), 1771-1780.
58. Loaiciga, H., R. J. Charbeneau, L. G. Everett, G. E. Fogg, B. F. Hobbs, and S. Rouhani (1992), Review of ground-water quality monitoring network design, J. Hydraul. Eng., 118(1), 11-37. (Pubitemid 23374289)
59. Mahar, P., and B. Datta (1997), Optimal monitoring network and groundwater pollution source identification, J. Water Resour. Plann. Manage., 123(4), 199-207. (Pubitemid 127629166)
60. Massmann, J., and R. Freeze (1987), Groundwater contamination from waste management sites: The interaction between risk-based engineering design and regulatory policy 1. Methodology, Water Resour. Res., 23(2), 351-367. (Pubitemid 17070171)
61. Maxwell, R., and W. Kastenberg (1999), A model for assessing and managing the risks of environmental lead emissions, Stochastic Environ. Res. Risk Assess., 13(4), 231-250. (Pubitemid 129340133)
62. Maxwell, R., and S. Kollet (2008a), Interdependence of groundwater dynamics and land-energy feedbacks under climate change, Nat. Geosci., 1, 665-669.
63. Maxwell, R. M., and S. J. Kollet (2008b), Quantifying the effects of three-dimensional subsurface heterogeneity on hortonian runoff processes using a coupled numerical, stochastic approach, Adv. Water Resour., 31(5), 807-817.
64. Maxwell, R., C. Welty, and R. Harvey (2007), Revisiting the cape cod bacteria injection experiment using a stochastic modeling approach, Environ. Sci. Technol., 41(15), 5548-5558. (Pubitemid 47224838)
65. Maxwell, R., F. S. Carle, and F. B. Tompson (2008), Contamination, risk, and heterogeneity: On the effectiveness of aquifer remediation, Environ. Geol., 54(8), 1771-1786.
66. McKinney, D., and D. P. Loucks (1992), Network design for predicting groundwater contamination, Water Resour. Res., 28(1), 133-147.
67. Meyer, P. D., and E. D. Brill Jr. (1988), A method for locating wells in a groundwater monitoring network under conditions of uncertainty, Water Resour. Res., 24(8), 1277-1282.
68. Meyer, P. D., A. J. Valocchi, and J. W. Eheart (1994), Monitoring network design to provide initial detection of groundwater contamination, Water Resour. Res., 30(9), 2647-2659.
69. Montas, H., R. Mohtar, A. Hassan, and F. AlKhal (2000), Heuristic space-time design of monitoring wells for contaminant plume characterization in stochastic flow fields, J. Contam. Hydrol., 43(3-4), 271-301.
70. Moss, M. E. (1979a), Space, time, and the third dimension (model error), Water Resour. Res., 15(6), 1797-1800.
71. Moss, M. E. (1979b), Some basic considerations in the design of hydrologic data networks, Water Resour. Res., 15(6), 1673-1676.
72. Nicklow, J., et al. (2010), State of the art for genetic algorithms and beyond in water resources planning and management, J. Water Resour. Plann. Manage., 136(4), 412-432.
73. Nunes, L. M., M. C. Cunha, and L. Ribeiro (2004), Groundwater monitoring network optimization with redundancy reduction, J. Water Resour. Plann. Manage., 130(1), 33-43. (Pubitemid 38122776)
74. Nunes, L. M., M. Cunhia, and L. Ribeiro (2005), Optimal space-time coverage and exploration costs in groundwater monitoring networks, Environ. Monit. Assess., 93(1-3), 103-124. (Pubitemid 38418096)
75. Olea, R. (1984), Sampling design optimization for spatial functions, Math. Geol., 16(4), 365-391.
76. Pelikan, M., and D. E. Goldberg (2003), A hierarchy machine: Learning to optimize from nature and humans, Complexity, 8(5), 36-45.
77. Reed, P., and B. S. Minsker (2004), Striking the balance: Long-term groundwater monitoring design for conflicting objectives, J. Water Resour. Plann. Manage., 130(2), 140-149.
78. Reed, P., B. S. Minsker, and A. J. Valocchi (2000), Cost effective longterm groundwater monitoring design using a genetic algorithm and global mass interpolation, Water Resour. Res., 36(12), 3731-3741.
79. Reed, P. M., J. B. Kollat, M. P. Ferringer, and T. G. Thompson (2008), Parallel evolutionary multi-objective optimization on large, heterogeneous clusters: An applications perspective, J. Aerosp. Comput. Inf. Commun., 5, 460-478.
80. Richards, L. (1931), Capillary conduction of liquids through porous media, Physics, 1(5), 318-333.
81. Rizzo, D., D. Dougherty, and M. Yu (2000), An adaptive monitoring and operations system (aLTMOs™) for environmental management, in Proceedings 2000 ASCE Joint Conference on Water Resources Engineering and Water Resources Planning and Management, edited by R. H. Hotchkiss and M. Glade, pp. 113-123, ASCE, Minneapolis, Minn.
82. Rouhani, S. (1985), Variance reduction analysis, Water Resour. Res., 21(6), 837-846.
83. Ruiz-Cardenas, R., M. Ferreira, and A. Schmidt (2009), Stochastic search algorithms for optimal design of monitoring networks, Environmetrics, 21(1), 102-112.
84. Saad, Y. (2003), Iterative Methods for Sparse Linear Systems, 2 ed., SIAM, Philadelphia, Penn.
85. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6(2), 461-464.
86. Shah, R. (2010), Using hierarchical Bayesian optimization to learn and exploit the dependency structures of combinatorial many-objective decision problems, M.S. thesis, Dep. of Ind. Eng. and Oper. Res., Pa. State Univ., University Park, Pa.
87. Storck, P., J. W. Eheart, and A. J. Valocchi (1997), A method for the optimal location of monitoring wells for detection of groundwater contamination in three-dimensional aquifers, Water Resour. Res., 33(9), 2081-2088.
88. Sun, N. Z., and W. W. G. Yeh (2007), Development of objective-oriented groundwater models: 2. robust experimental design, Water Resour. Res., 43, W02421, doi:10.1029/2006WR004888.
89. Tang, Y., P. Reed, and J. B. Kollat (2007), Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications, Adv. Water Resour., 30(3), 335-353.
90. Task Committee on Long-Term Groundwater Monitoring Design (2003), Long-Term Groundwater Monitoring: The State of the Art, ASCE, Reston, Va.
91. Tompson, A., E. Vomoris, and L. Gelhar (1987), Numerical simulation of solute transport in randomly heterogeneous porous media: Motivation, model development, and application, Tech. Rep. UCUD-21821, Lawrence Livermore Natl. Lab., Livermore, Calif.
92. Tompson, A., R. Ababou, and L. Gelhar (1989), Implementation of the three-dimensional turning bands random field generator, Water Resour. Res., 25(10), 2227-2243.
93. Tompson, A., R. Maxwell, S. Carle, M. Zavarin, G. Pawloski, and D. Shumaker (2005), Evaluation of the non-transient hydrologic source term from the CAMBRIC underground nuclear test in frenchman flat, nevada test site, Tech. Rep. UCRL-TR-217191, Lawrence Livermore Natl. Lab., Livermore, Calif.
94. Tompson, A. F., and D. E. Dougherty (1992), Particle-grid methods for reacting flows in porous media with application to Fisher's equation, Appl. Math. Modell., 16(7), 374-383.
95. Tompson, A. F., and L. W. Gelhar (1990), Numerical simulation of solute transport in three-dimensional randomly heterogeneous porous media, Water Resour. Res., 26(10), 2541-2562.
96. Vrugt, J., C. Diks, H. Gupta, W. Bouten, and J. Verstraten (2005), Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, doi:10.1029/2004WR003059.
97. Vrugt, J., P. Stauffer, T. Wohling, B. Robinson, and V. Vesselinov (2008), Inverse modeling of subsurface flow and transport properties: A review with new developments, Vadose Zone J., 7(2), 843-864.
98. Wagner, B. J. (1995), Sampling design methods for groundwater modeling under uncertainty, Water Resour. Res., 31(10), 2581-2591.
99. Welch, G., and G. Bishop (2004), An introduction to the Kalman filter, Tech. Rep. TR 95-041, Univ. of N. C., Chapel Hill.
100. Wilson, C., C. Einberger, R. Jackson, and R. Mercer (1992), Design of ground-water monitoring networks using the monitoring efficiency model (memo), Ground Water, 30(6), 965-970. (Pubitemid 23574844)
101. Wu, J., C. Zheng, and C. C. Chien (2005), Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions, J. Contam. Hydrol., 77(1-2), 41-65.
102. Yangxiao, Z., C. Te Stroet, and F. Van Geer (1991), Using kalman filtering to improve and quantify the uncertainty of numerical groundwater simulations 2. Application to monitoring network design, Water Resour. Res., 27(8), 1995-2006.
103. Zhang, Y., G. Pinder, and G. S. Herrera (2005), Least cost design of groundwater quality monitoring networks, Water Resour. Res., 41, W08412, doi:10.1029/2005WR003936..