Non-probabilistic uncertainty in subsurface hydrology and its applications: An overview

Water, Air, and Soil Pollution: Focus

Volumn 6, Issue 1-2, 2006, Pages 35-46

  Ozbek, Metin M ^a   Pinder, George F ^a  

^a University of Vermont   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY MATHEMATICS; GROUNDWATER FLOW; HYDROLOGY; SUBSURFACE FLOW; UNCERTAINTY ANALYSIS; WATER MANAGEMENT;

EID: 33744792097     PISSN: 15677230     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11267-005-9011-4     Document Type: Article

References (61)

1. Bandemer, H. and Gebhardt, A.: 2000., Bayesian fuzzy kriging. Fuzzy Sets and Systems 112, 405.
2. Bardossy, A., Bogardi, I. and Kelly, W. E.: 1988. Imprecise (fuzzy) information in geostatistics. Mathematical Geology 20 (4), 287-311.
3. Bardossy, A., Bogardi, I. and Duckstein, L., 1990a. Fuzzy regression in hydrology. Water Resources Research 26 (7), 1497-1508.
4. Bardossy, A., Bogardi, I. and Kelly, W. E.: 1990b. Kriging with imprecise (fuzzy) variogram. Mathematical Geology 22 (1), 63-79.
5. Bardossy, A., Bronstert, A. and Merz, B.: 1995. 1-, 2- and 3-dimensional modeling of water movement in the unsaturated soil matrix using a fuzzy approach. Advances in Water Resources 18, 237-251.
6. Bardossy, A. and Disse, M.: 1993. Fuzzy rule based models for infiltration. Water Resources Research 29, 373-382.
7. Bardossy, A. and Duckstein, L.: 1992. Analysis of a karstic aquifer management problem by fuzzy composite programming. Water Resources Bulletin 28 (1), 63-73.
8. Bezdek J., Keller, J., Krishnapuram, R. and Pal, N.: 1998. Fuzzy models and algorithms for pattern recognition and image processing. The Handbooks On Fuzzy Sets Kluwer. 776p.
9. Blazkova, S. and Beven, K.: 2002. Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty). Water Resources Research 38 (14), 1-14.
10. Bogardi, I., Bardossy, A., Duckstein, L.: 1983. Regional management of an aquifer for mining under fuzzy environmental objectives. Water Resources Research 19 (6) 1394-1402.
11. Cazemier, D. R., Lagacherie, P. and Martin-Clouaire R. M.: 2001. A possibility theory approach for estimating available water capacity from imprecise information contained in soil databases. Geoderma 103, 113-132.
12. Chanas, S. and Nowakowski, M.: 1988. Single value simulation of fuzzy variable. Fuzzy Sets and Systems 25, 43.
13. Chen, Z., Huang, G. H. and Chakma, A.: 2000. Risk assessment of a petroleum contaminated site through a multi-phase and multi-component modeling approach. Journal of Petroleum Science and Engineering 26, 273.
14. Chen, J. and Rubin, Y.: 2003. An effective Bayesian model for lithofacies estimation using geophysical data. Water Resources Research 39 (5), SBH. 4. 1-11.
15. Demicco, R. V. and Klir, G. J. (eds.).: 2003. Fuzzy Logic in Geology. Elsevier Academic Press 345p.
16. Dhiman, S. D. and Keshari, A. K.: 2003. Quantifying uncertainties using fuzzy logic for groundwater driven contaminant exposure assessment. In: Proceedings. Symposium. Groundwater Quality Modeling And Management Under Uncertainty. Mishra, S. (ed.)
17. Diamond, P.: 1989. Fuzzy kriging. Fuzzy Sets and Systems 33, 315-332.
18. Dong, W. and Shah, H. C.: 1987. Vertex method for computing functions of fuzzy variables. Fuzzy Sets and Systems 24, 65-78.
19. Dou, C., Woldt, W., Bogardi, I. and Dahab, M.: 1995. Steady state groundwater flow simulation with imprecise parameters. Water Resources Research 31, 2709-2719.
20. Dou, C., Woldt, W., Dahab, M. and Bogardi, I.: 1997a. Transient ground-water flow simulation using a fuzzy set approach. Ground Water 35 (2), 205-215.
21. Dou, C., Woldt, W., Bogardi, I. and Dahab M.: 1997b. Numerical solute transport simulation using fuzzy sets approach. Journal of Contaminant Hydrology 27, 107-126.
22. Dou, C., Woldt, W. and Bogardi, I.: 1999. Fuzzy rule-based approach to describe solute transport in the unsaturated zone. Journal of Hydrology 220, 74-85.
23. Dubois, D. and Prade, H.: 1985. A review of fuzzy set aggregation connectives. Information Systems 36, 85.
24. Dubois, D. and Prade, H.: 1988. Possibility Theory. Plenum Press. New York. 263p.
25. Dubois, D. and Prade, H.: 1996. What are fuzzy rules and how to use them? Fuzzy Sets and Systems 84, 169.
26. Dubois, D., Prade, H. and Ughetto, L.: 2003. A new perspective on reasoning with fuzzy rules. International Journal of Intelligent Systems 18, 541.
27. Franks, S. W. and Beven, K. J.: 1999. Conditioning a multiple-patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely sensed data. Water Resources Research 35 (9), 2751-2761.
28. Guan, J. and Aral, M.: 2004. Optimal design of groundwater remediation systems using fuzzy set theory. Water Resources Research 40.
29. Hong, Y- S., Rosen, M. R. and Reeves, R. R.: 2002. Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers. Journal of Hydrologic Engineering 7, 380-391.
30. Hu, Z. Y., Huang, G. H., and Chan, C. W.: 2003. A fuzzy process controller for in situ groundwater bioremediation. Engineering Applications of Artificial Intelligence 16, 131-147.
31. Jairaj, P. G. and Vedula, S. 2000. Multireservoir system optimization using fuzzy mathematical programming. Water Resources Management 14, 457.
32. Janakiraman, K. K. and Konno, M.: 2002. Cross-borehole geological interpretation model based on a fuzzy neural network and geotomography. Geophysics 67, 1177-1183.
33. Kindler, J., Tyszewski, S. and Zielinski, P.: 1984. Water resources allocation with imprecise demand estimates In: Proceedings, Bridge Between Control Science and Technology. Ninth Triennial World Congress of IFAC 6, 31-39.
34. Klir, G. and Wierman, M. J.: 1998. Uncertainty-Based Information: Elements Of Generalized Information Theory. Studies in Fuzziness and Soft Computing 15, Physica, New York.
35. Lee, Y., Dahab, M. F. and Bogardi, I.: 1992. Nitate risk management under uncertainty. Journal of Water Resources Planning and Management 118 (2), 151-165.
36. Lindsey, S. D., Gunderson, R. W. and Riley, J. P.: 1992. Spatial distribution of point soil moisture estimates using Landsat TM data and fuzzy-C classification. Water Resources Bulletin 28, 865-875.
37. Mackay, D. S., Samanta, S., Nemani, R. R. and Band, L. E.: 2003. Multi-objective parameter estimation for simulating canopy transpiration in forested watersheds. Journal of Hydrology 277, 230-247.
38. Martin-Clouaire, R., Cazemier, D. R. and Lagacherie, P.: 2000. Representing and processing uncertain soil information for mapping soil hydrological properties. Computers and Electronics in Agriculture 29, 41-57.
39. Morisawa, S. and Inoue, Y.: 1991. Optimum allocation of monitoring wells around a solid landfill site using precursor indicators and fuzzy utility functions. Journal Of Contaminant Hydrology 7, 337-370.
40. Mujumdar, P. P. and Sasikumar, K.: 2002. A fuzzy risk approach for seasonal water quality management of a river system. Water Resources Research 38 (1), 51.
41. Nedungadi, A., Bagtzoglou, A. C. and Sagar, B.: 1994. A fuzzy methodology to solve the groundwater flow problem in the presence of uncertain hydrogeological data. In: Proceedings, Applications Of Artificial Intelligence In Engineering, Ninth International Conference 19-21.
42. Nikravesh, M. and Aminzadeh, F.: 2001. Mining and fusion of petroleum data with fuzzy logic and neural network agents. Journal of Petroleum Science and Engineering 29, 221-238.
43. Ozbek, M. M., Pinder, G. F., Harris, R. H.: 2003. Water quality health risk analysis and fuzzy logic. Department of Civil and Environmental Engineering, University of Vermont. RCGRD Report 12-03-1.
44. Ozbek, M. M. and Pinder, G. F.: 2004a. Groundwater management with insufficient data. Department of Civil and Environmental Engineering, University of Vermont. RCGRD Report 9-04-1.
45. Ozbek, M. M. and Pinder, G. F. 2004b. A New Perspective on Modeling Groundwater Contaminant-Driven Health Risk with Subjective Information. Computational Methods in Water Resources XV, Chapel Hill, NC.
46. Pedrycz, Kandel, W. A. and Zhang, Y. Q.: 1998. Neurofuzzy systems. In: Fuzzy Systems Modeling and Control. The Handbooks on Fuzzy Sets 311-380. Kluwer.
47. Ponnambalam, K., Karray, F. and Mousavi, S. J.: 2003. Minimizing variance of reservoir systems operations benefits using soft computing tools. Fuzzy Sets and Systems 139, 451-461.
48. Ross, J., Ozbek, M. M. and Pinder, G. F.: 2004. Spatial Reasoning of a Multi-dimensional K-field Using a Possibilistic Interpretation of Expert Knowledge. Department of Civil and Environmental Engineering, University of Vermont. RCGRD Report 9-04-2.
49. Sakawa, M.: 1986. Interactive multiobjective decision making using fuzzy satisficing intervals and its application to an urban water resources system. Large Scale Systems 10, 203-213.
50. Samanta, S. and Mackay, D. S.: 2003. Flexible automated parameterization of hydrologic models using fuzzy logic. Water Resources Research 39, SWC 1-13.
51. Seibert, J. and McDonnell, J. J.: 2002. On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration. Water Resources Research 38, 23 1-14.
52. Shrestha, B. P., Duckstein, L. and Stakhiv, E. Z.: 1996. Fuzzy rule-based modeling of reservoir operation. Journal of Water Resources Planning and Management 122 (4), 262.
53. Stansbury, J., Bogardi, I. and Kelly, W. E.: 1992. Methodology for evaluating dredged material alternatives using risk-cost analysis under uncertainty In: Proceedings. Risk-Based Decision Making In Water Resources V. 236-259.
54. Triantafilis, J., Odeh, I. O. A., Minasny, B. and McBratney, A. B.: 2003. Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data. Environmental Modeling and Software 18, 667-680.
55. Woldt, W., Dahab, M., Bogardi, I. and Dou, C.: 1996. Management of diffusive pollution in groundwater under imprecise conditions using fuzzy models. Water Science and Technology 33, 4-5.
56. Wu, J. X., Shen, H. T. and Chen-You, Y.: 2001. Sediment classification and estimation of suspended sediment fluxes in the Changjiang estuary, China. Water Resources Research 37 (7), 1969-1979.
57. Zadeh, L. A.: 1968. Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23, 421-427.
58. Zadeh, L. A. 1978. Fuzzy sets as a basis for a theory of possibility. 1978. Fuzzy Sets and Systems 1, 3-28.
59. Zhu, A-X., Band, L. E., Dutton, B. and Nimlos, T. J.: 1996. Automated soil inference under fuzzy logic. Ecological modeling 90, 123-145.
60. Zhu, A. X. and Mackay, D. S.: 2001. Effects of spatial detail of soil information on watershed modeling. Journal of Hydrology 248, 54-77.
61. Znotinas, N. M. and Hipel, K. W.: 1979. Evaluation of alternatives to the Garrison Diversion Unit. Water Resources Bulletin 15, 354-368.