Development of water quality models for supporting NH3-N control in a dam regulated river

Water Science and Technology

Volumn 52, Issue 12, 2005, Pages 83-90

  Chung, S W ^a   Kim, J H ^b  

^a Chungbuk National University   (South Korea)

^b Korea Water Resources Corporation   (South Korea)

Author keywords

Ammonia nitrogen; Artificial neural network; Flow allocation; Multiple regression; Water quality modeling

Indexed keywords

CHEMICAL WATER TREATMENT; DAMS; FLOW OF WATER; MATHEMATICAL MODELS; NEURAL NETWORKS; NITROGEN COMPOUNDS; REGRESSION ANALYSIS; RIVERS; WATER TREATMENT PLANTS;

AMMONIA NITROGEN; FLOW ALLOCATION; MULTIPLE REGRESSION; WATER QUALITY MODELING;

WATER QUALITY;

AMMONIA; NITROGEN; RIVER WATER;

CHEMICAL WATER TREATMENT; DAMS; FLOW OF WATER; MATHEMATICAL MODELS; NEURAL NETWORKS; NITROGEN COMPOUNDS; REGRESSION ANALYSIS; RIVERS; WATER QUALITY; WATER TREATMENT PLANTS;

AMMONIUM COMPOUND; ARTIFICIAL NEURAL NETWORK; NUMERICAL MODEL; RIVER WATER; WATER QUALITY;

ALKALINITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; CONTROLLED STUDY; CORRELATION FUNCTION; FORECASTING; INTERMETHOD COMPARISON; MULTIPLE REGRESSION; PERFORMANCE; STATISTICAL MODEL; WATER FLOW; WATER QUALITY; WATER TEMPERATURE;

GEUM;

EID: 31844448104     PISSN: 02731223     EISSN: None     Source Type: Journal    
DOI: 10.2166/wst.2005.0433     Document Type: Article

References (17)

1. Alley, W. (1986). Regression approximation for transport model constraint sets in combined simulation-optimization studies. Wat. Resour. Res., 22(4), 581-586.
2. Ambrose, R.B., Wool, T.A., Jr. and Martin, J.L. (1993). The Water Quality Analysis Simulation Program, WASP5: Model Documentation. Environmental Research Laboratory, Athens, GA.
3. Brown, L. and Barnwell, T. (1987). The Enhanced Stream Water Quality Models QUAL2E and QUAL2E-UNCAS: Documentation and User's Manual, EPA/600/ 3-87/007, USEPA, Georgia, USA.
4. Chung, F., Sandhu, N., Wilson, D. and Finch, R. (1999). Modeling flow-salinity relationships in the Sacramento-San Joaquin Delta using artificial neural networks, Report OSP-99-1, Department of Water Resources, California, USA.
5. Chung, S.W. and Kim, J.H. (2002). Development of artificial neural network models supporting reservoir operation for the control of downstream water quality. Wat. Engr. Res., 3(2), 143-153.
6. Environmental Laboratory, U.S. Army Corps of Engineers, Waterway Experiment Station (1995). CE-QUAL-RIV1: A Dynamic, One-Dimensional (Longitudinal) Water Quality Model for Streams User's Mannual, MS, USA.
7. French, M.N., Krajewski, W.F. and Cuykendall, R.R. (1992). Rainfall forecasting in space and time using a neural network. J. of Hydrology, 137, 1-31.
8. Hsu, K.L., Gupta, H.V. and Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Wat. Resour. Res., 31(10), 2517-2530.
9. Irish, L.B., Jr, Barrett, M.E., Malina, Jr. and Charbeneau, R.J. (1998). Use of regression models for analyzing highway stormwater loads. J. Environ. Eng., 124(10), 987-993.
10. Karunanithi, N., Grenney, W.J., Whitley, D. and Bovee, K. (1994). Neural networks for river flow prediction. J. Comp. in Civ. Engrg., 8(2), 201-220.
11. Kim, J.H. (1993). A study on hydrological forecasting of streamflows by using artificial neural network. Ph. D. Thesis, Dept. of Civil Engrg., Inha Univ., Korea.
12. Kim, J.H., Kang, K.W. and Park, C.Y. (1992). Nonlinear forecasting of streamflows by pattern recognition method. Korean J. of Hydroscience, 25(3), 105-113.
13. Lisboa, P.G.J. (1992). Neural Networks. Chapman & Hall, London.
14. Martin, J.L. and McCutcheon, S.C. (1999). Hydrodynamics and Transport for Water Quality Modeling, Lewis Publishers, New York.
15. Shamseldin, A.Y. (1997). Application of a neural network technique to rainfall-runoff modeling. J. Hydro., 199, 272-294.
16. Zealand, C.M., Burn, D.H. and Simonovic, S.P. (1999). Short term streamflow forecasting using artificial neural networks. J. Hydro., 214, 32-48.
17. Zou, R., Lung, W.S. and Guo, H. (2002). Neural network embedded monte carlo approach for water quality modeling under input information uncertainty. J. of Comput. in Civ. Eng., 16(2), 135-142.