Prediction of urban stormwater quality using artificial neural networks

Environmental Modelling and Software

Volumn 24, Issue 2, 2009, Pages 296-302

  May, Daniel B ^a   Sivakumar, Muttucumaru ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

Artificial neural network; Chemical oxygen demand; Lead; Regression; Stormwater runoff; Suspended solids; Total Kjeldhal nitrogen; Total phosphorus; Urban catchment; Water quality

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; CATCHMENTS; CHEMICAL OXYGEN DEMAND; FORECASTING; LEARNING ALGORITHMS; LINEAR EQUATIONS; NETWORK PROTOCOLS; NEURAL NETWORKS; OXYGEN; PHOSPHORUS; SENSOR NETWORKS; STORM SEWERS;

ARTIFICIAL NEURAL NETWORK; LEAD; REGRESSION; STORMWATER RUNOFF; SUSPENDED SOLIDS; TOTAL KJELDHAL NITROGEN; TOTAL PHOSPHORUS; URBAN CATCHMENT; WATER QUALITY;

REGRESSION ANALYSIS;

ARTIFICIAL NEURAL NETWORK; CATCHMENT; CHEMICAL OXYGEN DEMAND; LEAD; NITROGEN; PHOSPHORUS; REGRESSION ANALYSIS; RUNOFF; STORMWATER; WATER QUALITY;

NORTH AMERICA; UNITED STATES;

EID: 53149113747     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2008.07.004     Document Type: Article

References (24)

1. Athayde D.N., Shelley P.E., Driscoll E.D., Gaboury D., and Boyd G. Results of the Nationwide Urban Runoff Program vol. 1 (1983), U.S. Environmental Protection Agency, Washington
2. Black P.E. Watershed Hydrology (1996), Lewis Publishers, Boca Raton
3. Brezonik P.L., and Stadelmann T.H. Analysis and predictive models of stormwater runoff volumes, loads and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA. Water Research 36 (2002) 1743-1757
4. NURP Data (1998), Cahaba/Warrier Student Chapter of the American Water Resources Association (University of Alabama). http://www.eng.ua.edu/∼awra/download.htm
5. Corbett C.W., Wahl M., Porter D.E., Edwards D., and Moise C. Nonpoint source runoff modeling: A comparison of a forested watershed and an urban watershed on the South Carolina coast. Journal of Experimental Marine Biology and Ecology 213 (1997) 133-149
6. Driver N., and Tasker G. Techniques for estimation of storm runoff loads, volumes and selected constituent concentrations in urban watersheds in the Unites States. United States Geological Survey Water-Supply Paper 2363 (1990), United States Government Printing Office, Washington 177-246
7. Goethals P.L.M., Dedecker A.P., Gabriels W., Lek S., and De Pauw N. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology 41 3 (2007) 491-508
8. Holmberg M., Forsius M., Starr M., and Huttunen M. An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change. Ecological Modelling 195 (2006) 51-60
9. Huber W.C. Prediction of urban nonpoint source water quality: methods and models. International Symposium on Urban Stormwater Management (1992), Institution of Engineers, Australia
10. Lek S., Guiresse M., and Giraudel J.L. Predicting stream nitrogen concentration from watershed features using neural network. Water Research 33 16 (1999) 3469-3478
11. Loke E., Arnbjerg-Nielsen K., and Harremoes P. Artificial neural networks and grey-box modelling: a comparison. In: Joliffe I.B., and Ball J.E. (Eds). Eighth International Conference: Urban Storm Drainage Proceedings vol. 1 (1999), The Institution of Engineers Australia, Australia
12. Maier H.R., and Dandy G.C. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research 32 4 (1996) 1013-1022
13. Maier H.R., and Dandy G.C. The effect of internal network parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environmental Modelling and Software 13 (1998) 193-209
14. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling and Software 15 (2000) 101-124
15. May D., and Sivakumar M. Techniques for predicting total phosphorus in urban stormwater runoff at unmonitored catchments. Journal of Australia and New Zealand Industrial and Applied Mathematics (ANZIAM) 45 E (2003) C296-C309
16. Mazvimavi D., Meijerink A.M.J., Savenije H.H.G., and Stein A. Prediction of flow characteristics using multiple regression and neural networks: a case study in Zimbabwe. Physics and Chemistry of the Earth 30 (2005) 639-647
17. National Atmospheric Deposition Program (1994), National Trends Network. http://nadp.sws.uiuc.edu (accessed 15 January 2003)
18. Riad S., Mania J., Bouchaou L., and Najjar Y. Rainfall-runoff model using an artificial neural network approach. Mathematical and Computer Modelling 40 (2004) 839-846
19. Sarangi A., and Bhattacharya A.K. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management 78 (2005) 195-208
20. Sliva L., and Williams D.D. Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Research 35 14 (2001) 3462-3472
21. Tayfur G., Swiatek D., Wita A., and Singh V.P. Case study: finite element method and artificial neural network models for flow through Jeziorsko Earthfill dam in Poland. Journal of Hydraulic Engineering 131 6 (2005) 431-440
22. Van Buren M.A., Watt W.E., and Marsalek J. Application of the log-normal and normal distributions to stormwater quality parameters. Water Research 31 1 (1997) 95-104
23. Vaze J., and Chiew F. Experimental study of pollutant accumulation on an urban road surface. Urban Water 4 (2002) 379-389
24. In: Ward A.D., and Elliot W.J. (Eds). Environmental Hydrology (1995), Lewis Publishers, Boca Raton