Comparing the stream re-aeration coefficient estimated from ANN and empirical models

Hydrological Sciences Journal

Volumn 50, Issue 6, 2005, Pages 1037-1052

  Jain, Sharad Kumar ^a   Jha, Ramakar ^a  

^a NATIONAL INSTITUTE OF HYDROLOGY   (India)

Author keywords

Artificial neural networks; Dissolved oxygen; India; Kali River; Re aeration coefficient; Stream; Water quality

Indexed keywords

MATHEMATICAL MODELS; NEURAL NETWORKS; OXYGEN; SHEAR STRESS; STREAM FLOW; WATER QUALITY;

DISSOLVED OXYGEN; RE-AERATION COEFFICIENT;

RIVERS;

AERATION; ARTIFICIAL NEURAL NETWORK; DISSOLVED OXYGEN; STREAMWATER; WATER QUALITY;

ASIA; EURASIA; INDIA; KALI RIVER; SOUTH ASIA;

EID: 28544447846     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1623/hysj.2005.50.6.1037     Document Type: Article

References (56)

1. APHA (1985) Standard methods for the examination of water and wastewater (sixteenth edn.). American Public Health Association, Washington DC, USA.
2. ASCE Task Committee (2000a) Artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Engng ASCE 5(2), 115-123.
3. ASCE Task Committee (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. J. Hydrol. Engng ASCE 5(2), 124-137.
4. Bhargava, D. S. (1983) Most rapid BOD assimilation in Ganga and Yamuna. rivers. J. Environ. Engng ASCE 109(1), 174-188.
5. Cadwallader, T. E. & McDonnell, A. J. (1969) A multivariate analysis of re-aeration data. Water Res. 3, 731-742.
6. Campolo, M., Soldati, A. & Andreussi, P. (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrol. Sci. J. 48(3), 381-398.
7. Cannon, A. J. & Whitfield, P. H. (2001) Modelling transient pH depressions in coastal streams of British Columbia using neural networks. J. Am. Water Resour. Assoc. 37(1), 73-89.
8. Chibanga, R., Berlamont, J. & Vandewalle, J. (2003) Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin. Hydrol. Sci. J. 48(3), 363-379.
9. Churchill, M. A., Elmore, H. L. & Buckingham, R. A. (1962) The prediction of stream reaeration rates. J. Sanitary Engng Div. ASCE 88(SA4), 1-46.
10. Cigizoglu, H. K. (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol. Sci. J. 48(3), 349-361.
11. Dawson, C. W. & Wilby, R. (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrol. Sci. J. 43(1), 47-66.
12. Deka, P. & Chandramouli, V. (2003) A fuzzy neural network model for deriving the river stage-discharge relationship. Hydrol. Sci. J. 48(2), 197-209.
13. Hren, J. (1994) Determination of reaeration coefficients for Ohio streams. US Geol. Survey WRI Report 84-4139, Washington, DC, USA.
14. Hsu, K-L., Gupta, H. V. & Sorooshian, S. (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 31(10), 2517-2530.
15. Huang, W. & Foo, S. (2002) Neural network modelling of salinity variation in Apalanchicola River. Water Research 36, 356-362.
16. Hutton, P. H., Sandhu, N. & Chung, F. I. (1996) Predicting THM formation with artificial neural networks. Proc. North Am. Water and Envir. Conf., 3547-3556. Am. Soc. Civil Engrs.
17. Imrie, C. E., Durucan, S. & Korre, A. (2000) River flow prediction using artificial neural networks: generalization beyond the calibration range. J. Hydrol. 233, 138-153.
18. Jain, S. K. (2001) Development of integrated sediment rating curves using artificial neural networks. J. Hydraul. Engng ASCE 127(1), 30-37.
19. Jain, S. K. & Chalisgaonkar, D. (2000) Setting up stage-discharge relations using ANN. J. Hydrol. Engng ASCE 5(4), 428-433.
20. Jain, S. K., Das, A. & Srivastava, D. K. (1999) Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plan. Manage. ASCE 125(5), 263-271.
21. Jha, R., Ojha, C. S. P. & Bhatia, K. K. S. (2001) Refinement of predictive reaeration equations for a typical Indian river. Hydrol. Processes 15(6), 1047-1060.
22. Jha, R., Ojha, C. S. P. & Bhatia, K. K. S. (2004) A supplementary approach for estimating reaeration rate coefficients. Hydrol. Processes 18, 65-79.
23. Jolankai, G. (1997) Basic river water quality models. IHP-V, Technical Documents in Hydrology, no. 13, UNESCO, Paris.
24. Karunanithi, N., Grenney, W. J., Whitley, D. & Bovee, K. (1994) Neural networks for river flow prediction. J. Comput. in Civil Engng ASCE 8(2), 210-220.
25. Kişi, Ö. (2004) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment prediction and estimation. Hydrol. Sci. J. 49(6), 1025-1040.
26. Krenkel, P. A. & Orlob, G. T. (1962) Turbulent diffusion and the reaeration coefficient. J. Sanit. Engng Div. ASCE 88(SA2), 53-83.
27. Langbein, W. B. & Durum, W. H. (1967) The aeration capacity of streams. US Geol. Survey Circular no. 542, Washington DC, USA.
28. Lewis, W. W. & Whitman, W. G. (1924) Principles of gas absorption. Indust. Engng Chem. 16(2), 1215-1220.
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(4), 1013-1022.
30. Maier, H. R. & Dandy, G. C. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Model. Software 15, 101-124.
31. Minns, A. W. & Hall, M. J. (1996) Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 41(3), 399-418.
32. Moog, D. B. & Jirka, G. H. (1998) Analysis of reaeration equations using mean multiplicative error. J. Environ. Engng ASCE 112(2), 104-110.
33. O'Connor, D. J. & Dobbins, W. E. (1958) Mechanism of reaeration in natural streams. Trans. ASCE 123, 641-684.
34. Owens, M., Edwards, R. W. & Gibbs, J. W. (1964) Some reaeration studies in streams. J. Air Water Pollut. 8(8/9), 469-486.
35. Parker, G. W. & DeSimone, I. A. (1992) Estimating reaeration coefficients for low-slope streams in Massachusetts and New York, 1985-86. US Geol. Survey Report no. WRI 91-4188.
36. Parkhurst, J. D. & Pomeroy, R. D. (1972) Oxygen absorption in streams. J. Sanit. Engng Div, ASCE 98(1), 101-124.
37. Rao, A. R. (1999) Prediction of reaeration rates in square, stirred tanks. J. Environ. Engng ASCE 125(3), 215-223.
38. Roehl, E. A. & Conrads, P. A. (1999) Real-time control for matching wastewater discharges to the assimilative capacity of a complex, tidally affected river basin. In: Proc. 1999 South Carolina Environmental Conference (Myrtle Beach, South Carolina, USA, 15-16 March 1999. http://smig.usgs.gov/SMIG/features_0399/nnm2.html.
39. Ruhl, K. J. & Smoot, J. L. (1987) Mean velocity, longitudinal dispersion and reaeration characteristics of selected streams in the Kentucky river basins. US Geol. Survey Water Resources Investigation Report 87-4179, Louisville, Kentucky, USA.
40. Rumelhart, D. E., McLelland, J. L. & the PDP Research Group (1986) Parallel Distributed Processing - Explorations in the Micro Structure of Cognition, vol. I: Foundations. The MIT Press, Cambridge, Massachusetts, USA.
41. Sajikumar, S. & Thandaveswara, B. S. (1999) A non-linear rainfall-runoff model using an artificial neural network. J. Hydrol. 216, 32-55.
42. Sandhu, N. & Finch, R. (1996) Emulation of DWRDSM using artificial neural networks and estimation of Sacramento River flow from salinity. Proc. North Am. Water and Env. Conf. (New York), 4335-4340. Am. Soc. Civil Engrs.
43. Smoot, J. L. (1988) An examination of stream reaeration coefficients and hydraulic conditions in a pool and riffle stream. PhD Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA.
44. Solomatine, D. P. & Dulal, K. N. (2003) Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 48(3), 399-411.
45. Streeter, H. W. & Phelps, E. B. (1925) A study of the pollution and natural purification of the Ohio river. Public Health Bull. no. 146. Public Health Service, Washington DC, USA.
46. Sudheer, K. P. (2005) Knowledge extraction from trained neural network river flow models. J. Hydrol. Engng ASCE 10(4), 264-269.
47. Sudheer, K. P. & Jain, A. (2004) Explaining the internal behaviour of artificial neural network river flow models. Hydrol. Processes 18, 833-844.
48. Texas Water Development Board (1971) Simulation of water quality in streams and canals. Report no. 128, Texas Department of Water Resources, Austin, USA.
49. Thackston, E. L. & Dawson, J. W. (2001) Recalibration of a reaeration equation. J. Environ. Engng ASCE 127(4), 317-321.
50. Thackston, E. L. & Krenkel, P. A. (1969) Reaeration prediction in natural streams. J. Sanit. Engng Div. ASCE 95(1), 65-93.
51. Thomann, R. V. & Müller, J. A. (1987) Principles of Surface Water Quality Modelling and Control. Harper & Row Publishers, New York, USA.
52. Tsivoglou, E. C. & Wallace, J. R. (1972) Characterization of stream reaeration capacity. Report no. EPA-R3-72-012, US Environ. Protection Agency, Washington DC, USA.
53. Vemuri, V. R. (1992) Artificial Neural Networks: Concepts and Control Applications. IEEE Computer Society Press, California, USA.
54. Wilby, R. L., Abrahart, R. J. & Dawson, C. W. (2003) Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48(2), 163-181.
55. Wilcock, R. J. (1988) Study of river reaeration at different flow rates. J. Environ. Engng ASCE 114(1), 91-103.
56. Yegnanarayana, B. (1999) Artificial Neural Networks. Prentice-Hall of India Pvt. Ltd., New Delhi, India.