Application of artificial neural networks to assess pesticide contamination in shallow groundwater

Science of the Total Environment

Volumn 367, Issue 1, 2006, Pages 234-251

  Sahoo, Goloka B ^a   Ray, Chittaranjan ^a,b   Mehnert, Edward ^c   Keefer, Donald A ^c  

^a University of Hawaii at Manoa   (United States)

^b UNIVERSITY OF HAWAII   (United States)

^c ILLINOIS STATE GEOLOGICAL SURVEY   (United States)

Author keywords

Aquifer sensitivity; Artificial neural network; Groundwater quality; Pesticide occurrence; Shallow wells; Statewide monitoring network

Indexed keywords

CONCENTRATION (PROCESS); CONTAMINATION; NEURAL NETWORKS; PESTICIDES; SENSITIVITY ANALYSIS; WATER QUALITY;

AQUIFER SENSITIVITY; GROUNDWATER QUALITY; PESTICIDE OCCURRENCE; SHALLOW WELLS; STATEWIDE MONITORING NETWORK;

GROUNDWATER POLLUTION;

GROUND WATER; PESTICIDE;

ARTIFICIAL NEURAL NETWORK; GROUNDWATER POLLUTION; PESTICIDE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION ANALYSIS; DATA ANALYSIS; ERROR; INFORMATION PROCESSING; LEACHING; MOLECULAR MODEL; PREDICTION; PRIORITY JOURNAL;

ENVIRONMENTAL MONITORING; FRESH WATER; ILLINOIS; NEURAL NETWORKS (COMPUTER); PESTICIDES; PREDICTIVE VALUE OF TESTS; SEASONS; WATER POLLUTANTS, CHEMICAL; WATER SUPPLY;

EID: 33745982644     PISSN: 00489697     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2005.12.011     Document Type: Article

References (53)

1. Amari S., Murata N., Müller K.R., Finke M., and Yang H.H. Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans Neural Netw 8 5 (1997) 985-996
2. ASCE Task Committee. Artificial neural network in hydrology. J Hydrol Eng 5 2 (2000) 124-144
3. Barbash J.E., and Resek E.A. Pesticides in ground water: distribution, trends, and governing factors (1997), Lewis Publishers, Boca Raton (FL) 590
4. Barbash J.E., Thelin G.P., Kolpin D.W., and Gilliom R.J. Major herbicides in ground water: results from the National Water-Quality Assessment. J Environ Qual 30 (2001) 831-845
5. Birikundavyi S., Labib R., Trung H.T., and Rousselle J. Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7 5 (2002) 392-398
6. Brodnjak-Vončina D., Dobčnik D., Novič M., and Zupan J. Chemometrics characterisation of the quality of river water. Anal Chim Acta 462 (2002) 87-100
7. Burkart M.R., and Kolpin D.W. Hydrologic and land-use factors associated with herbicides and nitrate in near-surface aquifers. J Environ Qual 22 (1993) 646-656
8. Carsel R.F., Smith C.N., Mulkey L.A., Dean J.D., and Jowsie P. Pesticide root zone model (PRZM). Release 1, EPA-600/3-84-109 (1984), USEPA, Washington, DC
9. Cheng B., and Titterington D.M. Neural networks: a review from a statistical perspective. Stat Sci 9 1 (1994) 2-54
10. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309 (2005) 17-38
11. Fausett L. Fundamentals of neural networks (1994), Prentice Hall, Englewood Cliffs (NJ)
12. Flood I., and Kartam N. Neural Networks in civil engineering: I. Principles and understanding. J Comput Civ Eng 8 2 (1994) 131-148
13. Govindaraju R.S., and Rao A.R. Artificial neural networks in hydrology (2000), Kluwer Academic Publishers, Boston
14. Hagan M.T., Demuth H.P., and Beale M. Neural network design (1996), PWS Publishing, Boston
15. Hassoun M.H. Fundamentals of artificial neural networks (1995), MIT Press, Cambridge
16. Haykin S. Neural networks: a comprehensive foundation. Second edition (1999), Prentice Hall, Upper Saddle River (NJ)
17. Hecht-Nielsen R. Neurocomputing (1991), Addison-Wesley Publishing Company, New York
18. Holden L.R., and Graham J.A. The national Alachlor well water survey: project summary (1990), Monsanto Agricultural Company, St. Louis (MO) 26 pp.
19. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Netw 2 (1989) 359-366
20. Hsu K.L., Gupta H.V., and Soroosian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31 (1995) 2517-2530
21. Keefer D.A. Potential for agricultural chemical contamination of aquifers in Illinois: 1995 revision. Environmental Geology vol. 148 (1995), Illinois State Geological Survey, Champaign (IL) 28 pp.
22. Knisel WG. GLEAMS: groundwater loading effects of agricultural management systems, version 2.10, University of Georgia Coastal Plain Experimental Station, Tifton, GA, 1993.
23. Kolpin D.W., Thurman E.M., and Goolsby D.A. Occurrence of selected pesticides and their metabolites in near-surface aquifers of the Midwestern United States. Environ Sci Technol 30 (1996) 335-350
24. Kolpin D.W., Schnoebelen D.J., and Thurman E.M. Degradates provide insight to spatial and temporal trends of herbicides in Ground Water. Ground Water 42 4 (2004) 601-608
25. Krigbaum M. Evaluation of automated solid phase extractions of agrochemicals and industrial organic compounds from drinking water using U.S. EPA Method 525.2. Am Environ Lab 9 4 (1997) 12-14
26. Kudrycki TP. Neural network implementation of a medical diagnosis expert system. Master's thesis, College of Engineering, University of Cincinnati; 1988.
27. Lohninger H. Estimation of soil partition coefficients of pesticides from their chemical structure. Chemosphere 29 8 (1994) 1611-1626
28. Maier H.R., and Dandy G.C. The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32 (1996) 1013-1022
29. Maier H.R., and Dandy G.C. The effect of internal parameters and geometry on the performance of back propagation neural networks: an empirical study. Environ Model Softw 13 2 (1998) 193-209
30. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15 1 (2000) 101-124
31. Masters T. Practical neural network recipes in C++ (1993), Academic Press, San Diego
32. Mehnert E., Schock S.C., Barnhardt M.L., Caughey M.E., Chou S.F.J., Dey W.S., et al. The occurrence of agricultural chemicals in Illinois' rural private wells: results from the pilot study. Ground Water Monit Remediat 15 1 (1995) 142-149
33. Mehnert E., Keefer D.A., Dey W.S., Wehrmann H.A., Wilson S.D., and Ray C. Aquifer sensitivity to pesticide leaching: testing a soils and hydrogeologic index method. Ground Water Monit Remediat 25 4 (2005) 60-67
34. Mehnert E, Dey WS, Keefer DA, Wehrmann HA, Wilson SD, Ray C. Illinois' statewide monitoring well network for pesticides in shallow groundwater-network development and initial sampling results. Illinois State Geological Survey and Illinois State Water Survey Cooperative Groundwater Report 20, 64 pp., 2005b.
35. Mills PC, McMillan WD. Herbicides and their transformation products in source-water aquifers tapped by public-supply wells in Illinois, 2001-02. U.S. Geological Survey Water-Resources Investigations Report 03-4226, Urbana, IL, 57 pp., 2004.
36. Minns A.W., and Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41 3 (1996) 417-3999
37. Mishra A., Ray C., and Kolpin D.W. Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells. J Hydrol Eng 9 6 (2004) 502-511
38. Munch JW. Method 525.2, Determination of organic compounds in drinking water by liquid-solid extraction and capillary column gas chromatograph/mass spectrometry, revision 2.0. National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH; 1995. 60 pp. (available at http://www.ultrasci.com/Docs/500/525_2.PDF).
39. Pesticide Subcommittee of the Interagency Coordinating Committee on Groundwater. Illinois generic management plan for pesticides in groundwater, June 2000; 2000.
40. Principe J.C., Euliano N.R., and Lefebvre W.C. Neural and adaptive systems: fundamentals through simulations (1999), John Wiley & Sons, New York
41. Ray C. Pesticides in domestic wells (2003), American Society of Agricultural Engineers, St. Joseph ()I
42. Ray C., and Klindworth K.K. Neural networks for agrichemical vulnerability assessment of rural private wells. J Hydrol Eng 5 2 (2000) 162-171
43. Ray C., and Schock S.C. Comparability of large-scale studies of agricultural chemical contamination of rural private wells. Ground Water Monit Remediat 16 (1996) 92-102
44. Ripley B.D. Neural networks and related methods of classification. J R Stat Soc B 56 3 (1994) 409-456
45. Sahoo G.B., Ray C., and Wade H.F. Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks. Ecol Model 183 1 (2005) 29-46
46. Sarle W.S. Neural networks and statistical models. Proceedings of the nineteenth annual SAS users group international conference (1994), SAS Institute, Cary (NC) 1538-1550
47. Simunek J, Sejna M, van Genuchten R. HYDRUS-1D manual of versions 2.0. U.S. Salinity Laboratory, Riverside, CA; 1998.
48. Tamura S.I., and Tateishi M. Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Trans Neural Netw 8 2 (1997) 251-255
49. Tokar A.S., and Johnson P.A. Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng 4 3 (1999) 232-239
50. USDA-ARS. Root Zone Water Quality Model (RZQM) v 1.0. Technical Documentation, GSPR Report 2, USDA-ARS Great Plains Systems Research Unit, Fort Collins, CO; 1992.
51. USEPA. Implementing the food quality protection act, progress report. EPA 735-R-99001, Washington, DC, Office of Prevention, Pesticides, and Toxic Substances, USEPA; 1999.
52. USEPA. Drinking water standards and health advisories, United States Environmental Protection Agency, Office of Water, 2000; EPA 822-B-00-001, 12 pp.
53. Yang C.C., Prasher S.O., Lacroix R., and Kim S.H. A multivariate adaptive regression splines model for simulation of pesticide transport in soils. Biosyst Eng 86 1 (2003) 9-15