Neural networks modelling of streamflow, phosphorus and suspended solids: Application to the Canadian Boreal forest

Water Science and Technology

Volumn 53, Issue 10, 2006, Pages 91-99

  Nour, M H ^a   Smith, D W ^a   Gamal El Din, M ^a   Prepas, E E ^a,b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b LAKEHEAD UNIVERSITY   (Canada)

Author keywords

Kohonen neural networks; Modelling; Multilayer perceptron; Spectral Analysis; Streamflow total phosphorus; Total suspended solids

Indexed keywords

COMPUTER SIMULATION; FORESTRY; MONTE CARLO METHODS; OPTIMIZATION; PHOSPHORUS; SPECTRUM ANALYSIS; STREAM FLOW; SUSPENSIONS (FLUIDS); WATER QUALITY; WATER RESOURCES; WATERSHEDS;

CANADIAN BOREAL FOREST; KOHONEN NEURAL NETWORKS; STREAMFLOW TOTAL PHOSPHORUS;

MULTILAYER NEURAL NETWORKS;

PHOSPHORUS;

COMPUTER SIMULATION; FORESTRY; MONTE CARLO METHODS; MULTILAYER NEURAL NETWORKS; OPTIMIZATION; PHOSPHORUS; SPECTRUM ANALYSIS; STREAM FLOW; SUSPENSIONS (FLUIDS); WATER QUALITY; WATER RESOURCES; WATERSHEDS;

ARTIFICIAL NEURAL NETWORK; BOREAL FOREST; NUMERICAL MODEL; PHOSPHORUS; STREAMFLOW; WATERSHED;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANADA; CLIMATE CHANGE; COMPUTER MODEL; COMPUTER SIMULATION; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; COST BENEFIT ANALYSIS; INFORMATION PROCESSING; MONTE CARLO METHOD; PREDICTION; PROCESS OPTIMIZATION; STREAM (RIVER); SUSPENDED PARTICULATE MATTER; TAIGA; TIME SERIES ANALYSIS; WATER FLOW; WATER MANAGEMENT; WATER QUALITY; WATER SUPPLY; WATERSHED;

GEOLOGIC SEDIMENTS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PHOSPHORUS; RAIN; RIVERS; SNOW; TEMPERATURE; WATER MOVEMENTS; WATER POLLUTANTS;

FORESTS; OPTIMIZATION; PHOSPHORUS; SIMULATION; STREAM FLOW; SUSPENDED SOLIDS; WATER QUALITY; WATER RESOURCES; WATER SHEDS;

CANADA; NORTH AMERICA;

CYANOBACTERIA;

EID: 33745604026     PISSN: 02731223     EISSN: None     Source Type: Book Series    
DOI: 10.2166/wst.2006.302     Document Type: Article

References (10)

1. Brion, G.M. and Lingireddy, S. (2003). Artificial neural network modelling: a summary of successful applications relative to microbial water quality. Wat. Sci. Technol., 47(3), 235-240.
2. Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N. and Smith, V.M. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl., 8(3), 559-568.
3. Gong, N., Denoeux, T. and Bertand-Krajewski, J. (1996). Neural networks for solid transport modelling in sewer systems during storm events. Wat. Sci. Technol., 33(9), 85-92.
4. Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Macmillian College Publishing, NY.
5. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern., 43(l), 59-69.
6. Maier, H.R. and Dandy, G.C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and application. Environ. Modell. Softw., 15, 101-124.
7. Munn, N. and Prepas, E.E. (1986). Seasonal dynamics of phosphorus partitioning and export in two streams in Alberta, Canada. Can. J. Fish. Aquat. Sci., 43, 2464-2471.
8. Nour, M.H., Smith, D.W., Gamal El-Din, M. and Prepas, E.E. (2005). The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain, with emphasis on the role of wetlands. Ecol. Model. (in press).
9. Shumway, R.H. and Stoffer, D.S. (2000). Time Series Analysis and its Applications, Springer, New York.
10. Weber, D. and Englund, E. (1992). Evaluation and comparison of spatial interpolators. Math. Geol., 24, 381-391.