Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois

Journal of Hydroinformatics

Volumn 12, Issue 3, 2010, Pages 251-261

  Markus, Momcilo ^a   Hejazi, Mohamad I ^b   Bajcsy, Peter ^c   Giustolisi, Orazio ^d   Savic, Dragan A ^e  

^a University of Illinois at Urbana Champaign   (United States)

^b United States of America   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d POLITECNICO DI BARI   (Italy)

^e UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

Artificial neural networks; Drinking water; Forecasting; Genetic algorithms; Naive Bayes model; Nitrate N

Indexed keywords

EID: 78149451063     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2010.064     Document Type: Article

References (49)

1. Amenu, G. G., Markus, M., Kumar, P. & Demissie, M. 2007 Hydrologie applications of minimal resource allocation network (MRAN) algorithm. J. Hydrol. Eng. 12 (1), 124-129.
2. Bajcsy, P., Han, J., Liu, L. & Young, J. 2004 Survey of bio-data analysis from data mining perspective. In Data Mining Bioinformatics (ed. J. Wang, M. Zaki, H. Toivonen & D. Shasha), pp. 9-39. Springer-Verlag, Berlin.
3. Bajcsy, P., Kooper, R., Marini, L., Clutter, D. & Markus, M. 2006 Visualization and data mining tools applied to algal biomass prediction in Illinois streams. In Hydroinformatics 2006-Proceedings of the 7th International Conference on Hydroinformatics, Nice, France (ed. P. Gourbesville, J. Cunge, V. Guinot & S.-Y. Liong), pp. 926-933. Research Publishing Services, Chennai, India.
4. Benedetti, A., Lopez, P., Moreau, E., Bauer, P. & Venugopal, V. 2005 Verification of TMI-adjusted rainfall analyses of tropical cyclones at ECMWF using TRMM precipitation radar. J. Appl. Meteorol. 44 (11), 1677-1690.
5. Bobbin, J. & Recknagel, F. 2001 Inducing explanatory rules for the prediction of algal blooms by genetic algorithms. Environ. Int. 27 (2-3), 237-242.
6. Bowden, G. J., Dandy, G. C. & Maier, H. R. 2005 Input determination for neural network models in water resources applications. part 1. background and methodology. J. Hydrol. 301 (1-4), 75-92.
7. Bowden, G. J., Maier, H. R. & Dandy, G. C. 2005 Input determination for neural network models in water resources applications. part 2. case study: forecasting salinity in a river. J. Hydrol. 301 (1-4), 93-107.
8. Bowden, G. J., Nixon, J. B., Dandy, G. C., Maier, H. R. & Holmes, M. 2006 Forecasting chlorine residuals in a water distribution system using a general regression neural network. Math. Comput. Model. 44 (5-6), 469-484.
9. Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D. & Summers, R. M. 1992 The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay. Water Resour. Res. 28 (9), 2353-2363.
10. Davidson, J. W., Savic, D. A. & Walters, G. A. 1999 Method for the identification of explicit polynomial formulae for the friction in turbulent pipe flow. J. Hydroinf. 1 (2), 115-126.
11. Davidson, J. W., Savic, D. A. & Walters, G. A. 2000 Approximators for the Colebrook-White formula obtained through a hybrid regression method: computational methods in water resources. In Computational Methods, Surface Water Systems and Hydrology (ed. L. R. Bentley, J. F. Sykes, C. A. Brebbia, W. G. Gray & G. F. Pinder), Vol. 2, pp. 983-989. Balkema, Rotterdam.
12. Doglioni, A., Giustolisi, O., Savic, D. A. & Webb, B. W. 2008 An investigation on stream temperature analysis based on evolutionary computing. Hydrol. Process. 22 (3), 315-326.
13. Eder, B., Kang, D., Mathur, R., Yu, S. & Schere, K. 2006 An operational evaluation of the Eta-CMAQ air quality forecast model. Atmos. Environ. 40, 4894-4905.
14. French, M. N., Krajewski, W. F. & Cuykendall, R. R. 1992 Rainfall forecasting in space and time using a neural network. J. Hydrol. 137 (1-4), 1-31.
15. Giustolisi, O. & Laucelli, D. 2005 Improving generalization of artificial neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 50 (3), 439-457.
16. Giustolisi, O. & Savic, D. A. 2006 A symbolic data-driven technique based on evolutionary polynomial regression. J. Hydroinf. 8 (3), 207-222.
17. Giustolisi, O. & Simeone, V. 2006 Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions. Hydrol Sci. J. 51 (3), 502-523.
18. Giustolisi, O., Doglioni, A., Savic, D. A. & Webb, B. 2007 A multi-model approach to analysis of environmental phenomena (special issue). Complex. Integr. Resour. Manage. Environ. Modell. Softw. 5 (22), 674-682.
19. Giustolisi, O., Doglioni, A., Savic, D. A. & di Pierro, F. 2008 An evolutionary multi-objective strategy for the effective management of groundwater resources. Water Resour. Res. 44, W01403 (doi:10.1029/2006WR005359).
20. Goldberg, D. E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition. Addison-Wesley Longman, New York.
21. Guo, Y., Markus, M. & Demissie, M. 2002 Uncertainty of nitrate-N load computations for agricultural watersheds. Water Resour. Res. 38 (10), 3-1-3-12.
22. Haklander, A. J. & Van Delden, A. 2003 Thunderstorm predictors and their forecast skill for The Netherlands. Atmos. Res. 67 (8), 273-299.
23. Hall, M. J. & Minns, A. W. 1999 Classification of hydrologically homogeneous regions. Hydrol. Sci. J. 44 (5), 693-704.
24. Jain, S. K. 2008 Development of integrated discharge and sediment rating relation using a compound neural network. J. Hydrol. Eng. 13 (3), 124-131.
25. Keefer, L. & Demissie, M. 2000 Watershed Monitoring for the Lake Decatur Watershed 1998-1999. Illinois State Water Survey Contract Report 2000-2006, Champaign, IL.
26. Kohonen, T. 1988 An introduction to neural computing. Neural Netw. 1, 3-16.
27. Koza, J. R. 1992 Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge, MA.
28. Kumar, P., Alameda, J., Bajcsy, P., Folk, M. & Markus, M. 2006 Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling. CRC Press, Boca Raton, FL.
29. Maier, H. R. & Dandy, G. C. 1996 The use of artificial neural networks for the prediction of water quality parameters. Water Resour. Res. 32, 1013-1022.
30. Markus, M. 2005 Issues in designing automated minimal resource allocation neural networks. In IEEE Proceedings of the International Joint Conference on Neural Networks, 5, pp. 2671-2673.
31. Markus, M. 2006 Artificial neural networks. In Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling (ed. P. Kumar, J. Alameda, P. Bajcsy, M. Folk & M. Markus), pp. 411-438. CRC Press, Boca Raton, FL.
32. Markus, M., Tsai, C. W.-S. & Demissie, M. 2003 Uncertainty of weekly nitrate-nitrogen forecasts using artificial neural networks. J. Environ. Eng. 129 (3), 267-274.
33. Mathworks, Inc. 2007 MATLAB-The Language of Technical Computing. Available at: http://www.mathworks.com/products/matlab (August 14, 2008).
34. Mishra, A., Ray, C. & Kolpin, D. W. 2004 Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells. J. Hydrol. Eng. 9 (6), 502-511.
35. Moradkhani, H., Hsu, K.-L., Gupta, H. V. & Sorooshian, S. 2004 Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J. Hydrol. 295 (1-4), 246-262.
36. Muttil, N. & Lee, J. H. W. 2005 Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol. Modell. 189 (3-4), 363-376.
37. Ochoa-Rivera, J. C., García-Bartual, R. & Andreu, J. 2002 Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. Hydrol. Earth Syst. Sci. 6 (4), 641-654.
38. Ochoa-Rivera, J. C., Andreu, J. & García-Bartual, R. 2007 Influence of inflows modeling on management simulation of water resources system. J. Water Resour. Plann. Manage. 133 (2), 106-116.
39. Peng, F., Schuurmans, D. & Wang, S. 2004 Augmenting naive Bayes classifiers with statistical language models. Inf. Retr. 7 (3-4), 317-345.
40. Roebber, P. J., Bruening, S. L., Schultz, D. M. & Cortinas, J. V., Jr. 2002 Improving snowfall forecasting by diagnosing snow density. Weather Forecast. 18 (2), 264-287.
41. Salas, J. D., Markus, M. & Tokar, A. S. 2000 Streamflow forecasting based on artificial neural networks. In Artificial Neural Networks in Hydrology (ed. R. S. Govindaraju & A. R. Rao), pp. 23-51. Kluwer, Amsterdam.
42. Sharma, V., Negi, S., Rudra, R. & Yang, S. 2003 Neural networks for predicting nitrate-nitrogen in drainage water. Agric. Water Manage. 63 (3), 169-183.
43. Stenemo, F., Lindahl, A. M. L., Gardenas, A. & Jarvis, N. 2007 Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks. J. Contam. Hydrol. 93 (1-4), 270-283.
44. Suen, J.-P. & Eheart, J. W. 2003 Evaluation of neural networks for modeling nitrate concentrations in rivers. J. Water Resour. Plann. Manage. 129 (6), 505-510.
45. Thandaveswara, B. S. & Sajikumar, N. 2000 Classification of river basins using artificial neural network. J. Hydrol. Eng. 5 (3), 290-298.
46. Tokar, S. & Markus, M. 2000 Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng. 5 (2), 156-161.
47. USEPA (United States Environmental Protection Agency) 1991 National Primary Drinking Water Regulations: Final Rule. 40 CFR Parts 141, 142 and 143. Fed. Regist. 56(20), 3526-3597.
48. Yu, C., Northcott, W. J. & Mclsaac, G. F. 2004 Development of an artificial neural network for hydrologic and water quality modeling of agricultural watersheds. Trans. ASAE 47 (1), 285-290.
49. Zhang, B. & Govindaraju, R. S. 2003 Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds. J. Hydrol. 273 (1-4), 18-34.