Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: A GIS-based sensitivity analysis

Journal of Hydrology

Volumn 309, Issue 1-4, 2005, Pages 17-38

  Dixon, B ^a  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Fuzzy logic; GIS; GPS; Modeling; Neural networks; NO3 N

Indexed keywords

COMPUTER SOFTWARE; CONTAMINATION; ENVIRONMENTAL IMPACT; FUZZY SETS; GEOGRAPHIC INFORMATION SYSTEMS; HYDROLOGY; JAVA PROGRAMMING LANGUAGE; LAND USE; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS; NITROGEN OXIDES; REMOTE SENSING; SENSITIVITY ANALYSIS; STOCHASTIC PROGRAMMING; WATER POLLUTION;

GROUNDWATER VULNERABILITY; NEURO-FUZZY TECHNIQUES; NON-POINT SOURCE (NPS) POLLUTION; STOCHASTIC MODELS;

GROUNDWATER;

GROUNDWATER; NEURAL NETWORK; POLLUTION; WATER QUALITY;

GIS; GROUNDWATER; POLLUTION; WATER QUALITY;

EID: 20344385052     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2004.11.010     Document Type: Article

References (52)

1. L. Aller, T. Bennett, J. Lehr, R.J. Petty, and G. Hackett DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings, EPA/600/2-87/035 1987 US Environmental Protection Agency Ada, OK
2. Al-Rashidy S., 1999. Hydrogeologic controls of groundwater in the shallow manteled karst aquifer. Copperhead Spring, Savoy Experimental Watershed, Northwest Arkansas. MS Thesis. University of Arkansas.
3. P.A. Burrough Opportunities and limitations of GIS-based modeling of solute transport at the regional scale D.L. Corwin K. Loague Application of GIS to the non-point Source Pollutants in the Vadose Zone Special Publication # 48 1996 SSSA Madison, WI 19 38
4. P.A. Burrough, and R. McDonnell Principles of Geographical Information Systems 1998 Oxford University Press New York
5. P.A. Burrough, R.A. Macmillan, and W. Van Deursen Fuzzy classification methods for determining land suitability from soil profile observations and topography Jounal of Soil Science 43 2 1992 193 210
6. L.D. Connell, and G. Van den Daele A quantitative approach to aquifer vulnerability mapping Journal of Hydrology. 276 2003 71 88
7. D.L. Corwin, K. Loague, and T.R. Ellsworth Introduction to non-point source pollution in the vadose zone with advanced information technologies D.L. Corwin K. Loague T.R. Eilsworth Assessment of Non-point Source Pollution in the Vadose Zone Geophysical Monograph 108 108 1996 AGU Washington D.C.
8. Dixon B., 2001. Application of neuro-fuzzy techniques to predict ground water vulnerability in NW Arkansas. PhD Dissertation. University of Arkansas, Fayetteville.
9. B. Dixon Can Ground water Sampling Strategy Be Improved By Incorporating Fuzzy Logic in a GIS? Conference Proceeding, 25th Annual Applied Geography Conference, Binghamton, NY 2002
10. B. Dixon, and H.D. Scott Use of fuzzy logic with modified DRASTIC parameters to predict ground water contamination H.D. Scott Vulnerability and Use of Ground and Surface Waters in the Southern Mississippi Valley Region Vulnerability and Use of Ground and Surface Waters in the Southern Mississippi Valley Region 1998 16 51 (Pub # 269AWRC)
11. B. Dixon, H.D. Scott, J.C. Dixon, and K.F. Steele Prediction of aquifer vulnerability to pesticides using fuzzy rule-based models at the regional scale Physical Geography 23 2002 130 153
12. C. Dou, W. Woldt, M. Dahab, and I. Bogardi Transient groundwater flow simulation using a fuzzy set approach Ground Water 35 2 1997 205 215
13. C. Dou, W. Woldt, I. Bogardi, and M. Dahab Numerical solute transport simulation using fuzzy sets approach Journal of Contaminant Hydrology 27 1997 107 126
14. L. Fausett Fundamentals of Neural Networks 1994 Prentice-Hall New Jersey p. 460
15. R.S. Govindaraju Development of Geomorphological Artificial Neural Networks for Modeling Watershed Runoff Proceeding of 1998 Water and Watersheds Program Review, Corvallis, Oregon. EPA/NSF/USDA 1998 p. 59
16. D. Heebner, and L. Toran Sensitivity analysis of three-dimensional steady-state and transient spray irrigation models Ground Water 38 2000 20 28
17. R.M. Holger, and G.C. Dandy The use of artificial neural networks for the prediction of water quality parameters Water Resources Research 32 1996 1013 1022
18. E. Khan Neural fuzzy based intelligent systems and applications L.C. Jain N.M. Martin Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications 1999 CRC Press Washington, DC 331
19. R.L. Korthals, G.L. Hahn, and J.A. Nienaber Evaluation of neural networks as a tool for management of swine environments Transcation of American Society of Agricultural Engineers 37 4 1994 1295 1299
20. A. Kraslawski Review of applications of various types of uncertainty in chemical engineering Chemical Engineering and Processing 26 3 1989 185 191
21. A. Kraslawski, T. Koiranen, and L. Nystrom Concurrent engineering: robust design in fuzzy environment Computers and Chemical Engineering 17S 1993 447 452
22. Kresse T., 2000. ADEQ. Written communication.
23. H.S. Lin, and K.J. McInnes Water flow in clay soil beneath a tension infiltrometer Soil Science 159 1995 375 382
24. H.S. Lin, K.J. McInnes, L.P. Wilding, and C.T. Hallmark Effects of Soil Morphology on hydraulic properties: I. Quantification of soil morphology Soil Science Society of America Journal 63 1999 948 953
25. H. Liu, and V. Chandrashekar Classification of hydrometers based on polatrimetric radar measurements: development of fuzzy logic and neuro-fuzzy systems and in situ verifications Journal of Atmospheric and Oceanic Technology 17 2000 140 164
26. M. Lorrai, and G.M. Sechi Neural nets for modeling rainfall-runoff transformations Water Resources Management 9 1995 299 313
27. B. Mitra, J.M. McKimmey, H.D. Scott, and T.S. Hays Development and use of digital databases in agricultural research Trends in Agronomy 1 1997 1 17
28. National Research Council. (NRC) Ground water vulnerability assessment: contamination potential under conditions of uncertainty Committee on Techniques for Assessing Ground Water Vulnerability, Water Science and technology Board, Commission on Geosciences, Environment, and Resources 1993 National Academy Press Washington, DC p. 179
29. U. Nauck, and R. Kruse Design and Implementation of a neuro-fuzzy data analysis tool in JAVA. Manual 1999 Technical University of Braunschweig Braunschweig, Germany
30. Petersen J.C., Adamski J.C., Bell, Davis J.V., Femmer S.R., Freiwald D.A., Joseph R.L., 1998. U.S. Geological Survey Circular 1158. Available online from: http://water.usgs.gov/pubs/circ1158 Verified April 6 2002.
31. S.W. Piche The selection of weight accuracies for Madalines IEEE Transactions on Neural Networks 6 1995 432 445
32. V.L. Quisenberry, B.R. Smith, R.E. Phillips, H.D. Scott, and S. Nortcliff A soils classification system for describing water and chemical transport Soil Science 156 1993 306 315
33. A. Rashid, A. Aziz, and K-F. V. Wong A neural network approach to the determination of aquifer parameters Ground Water 30 1992 164 166
34. C. Ray, and K.K. Klindworth Neural networks for agrichemical vulnerability assessment of rural private wells Journal of Hydrologic Engineering 4 2000 162 171
35. A.N. Refenes, A. Zapranis, and G. Fransis Stock performance modeling using neural networks: a comparative study with regression models Neural Networks 7 2 1994 375 388
36. S.J. Rogers, H.C. Chen, D.C. Kopaska-Merkel, and J.H. Fang Predicting permeability from porosity using artificial neural networks AAPG Bulletin 79 1995 1786 1797
37. S.I. Sastri Uncertainty Analysis of Transport-Transformation Models. diss 1999 The Rutgers University New Brunswick New Jersey
38. T.J. Sauer, T.C. Daniel, D.J. Nichols, C.P. West, P.A. Moore Jr., and G.L. Wheeler Runoff quality from poultry litter-treated pasture and forests sites Journal of Environmental Quality 3 2000 515 521
39. M.G. Schaap, F.J. Leij, and M. Th. VanGenuchten Neural network analysis for hierarchical prediction of soil hydraulic properties Soil Science Society of American Journal 62 1998 847 855
40. L. See, and S. Openshaw A hybrid multi-model approach to river level forecasting Hydrological Science Journal 45 2000 523 536
41. C.R. Smith, and F.F. Steele Nitrate concentrations of ground water in Benton County, Arkansas AWRC Pub. #73 1990 University of Arkansas Fayetteville p. 48
42. Soil Division Staff Soil Survey Manual. USDA Handbook. No. 18 1993 U.S. Gov. Print. Office Washington, DC
43. J. Spiegelhalter A statistical view of uncertainty in expert systems W.A. Gale Artificial Intelligence and Statistics 1986 Addison-Wesley Reading, MA 17 55
44. D.Z. Sui A fuzzy GIS modeling approach for urban land evaluation Computers, Environment, and Urban Systems 16 2 1992 101 115
45. S. Tamari, J.H.M. Wosten, and J.C. Ruiz-Suarez Testing an artificial neural network for predicting soil hydraulic conductivity Soil Science Society of American Journal 57 1996 1088 1095
46. P. Walley Statistical Reasoning with Imprecise Probabilities 1991 Chapman & Hall London
47. F. Wang,, B. Hall, and G. Subaryono Fuzzy information representation and processing in conventional GIS software: database design and application International Journal of Geographical Information Systems 4 3 1990 261 283
48. S.N. Wood, and M.B. Thomas Super sensitivity to structure in biological models Proceedings of the Royal Society (B) 266 1999 565 570
49. W. Woldt, I. Dahab, C. Bogardi, and C. Dou Management of diffuse pollution in groundwater under imprecise conditions using fuzzy models Water Science and Technology 33 1996 249 257
50. Y.S. Wu, and P.A. Forsyth On the selection of primary variables in numerical formulation for modeling multiphase flow in porous media Journal of Contaminant Hydrology 48 3/4 2001 277 304
51. J. Yen, and R. Langaria Fuzzy Logic: Intelligence, Control and Information 1998 Prentice-Hall New Jersey
52. X.-Y. Zhu, S-Hi. Xu, J-J Zhu, Zhou N-Q, and C-Y Wu Study on the contamination of fracture - karst water in Boshan District, China Ground Water 35 1997 538 545