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

Ecological Modelling

Volumn 191, Issue 1, 2006, Pages 19-32

  Nour, Mohamed H ^a   Smith, Daniel W ^a   El Din, Mohamed Gamal ^a   Prepas, Ellie E ^a,b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b LAKEHEAD UNIVERSITY   (Canada)

Author keywords

Artificial neural networks; Boreal forest; Flow; Phosphorus; Time series; Watershed

Indexed keywords

ALGORITHMS; BACKPROPAGATION; DATABASE SYSTEMS; ECOSYSTEMS; PHOSPHORUS; TIME SERIES ANALYSIS; WETLANDS;

TERRESTRIAL BIODIVERSITY; WEATHER DATABASE;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BOREAL FOREST; ECOLOGICAL MODELING; NUTRIENT DYNAMICS; PHOSPHORUS; STREAMFLOW; WATERSHED; WETLAND;

CANADA; NORTH AMERICA;

SALIX;

EID: 28444499275     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2005.08.010     Document Type: Conference Paper

References (24)

1. L.E. Band, C.L. Tague, P. Groffman, and K. Belt Forest ecosystem processes at the watershed scale: hydrological and ecological controls of nitrogen export Hydrol. Process. 15 2001 2013 2028
2. G.M. Brion, and S. Lingireddy Artificial neural network modeling: a summary of successful applications relative to microbial water quality Water Sci. Technol. 47 3 2003 235 240
3. D.S. Chanasyk, I.R. Whitson, E. Mapfumo, J.M. Burke, and E.E. Prepas The impacts of forest harvest and wildfire on soils and hydrology in temperate forests: a baseline to develop hypotheses for the Boreal Plain J. Environ. Eng. Sci. 2 2003 S51 S62
4. C. Chatfield Time Series Forecasting 2001 Chapman & Hall, CRC Press New York 267 pp
5. S.L. Dingman Physical Hydrology 2nd ed 2002 Prentice-Hall Upper Saddle River, NJ 646 pp
6. A. Gamal El-Din, and D.W. Smith A neural network model to predict the wastewater inflow incorporating rainfall events Water Res. 36 5 2002 1115 1126
7. S. Haykin Neural Networks: A Comprehensive Foundation 2nd ed 1994 Macmillian College Publishing, Inc. New York 842 pp
8. S. Lek, I. Dimopoulos, and A. Fabre Predicting phosphorus and phosphorus load from watershed characteristics using back-propagation neural networks Acta Ecol. 17 1 1996 43 53
9. H.R. Maier, and G.C. Dandy Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and application Environ. Modell. Softw. 15 2000 101 124
10. H.R. Maier, and G.C. Dandy The use of artificial neural networks for the prediction of water quality parameters Water Resour. Res. 32 4 1996 1013 1022
11. H.R. Maier, G.C. Dandy, and M.D. Burch Use of artificial neural networks for modeling cyanobacteria Anabaena spp. in River Murray, South Australia Ecol. Modell. 105 1998 257 272
12. T. Masters Neural, Novel and Hybrid Algorithms for Time Series Prediction 1995 John Wiley and Sons 514 pp
13. P. McEachern, E.E. Prepas, J.J. Gibson, and W.P. Dinsmore Forest fire induced impacts on phosphorus, nitrogen, and chlorophyll a concentrations in boreal subarctic lakes Northern Alberta Can. J. Fish. Aquat. Sci. 57 2000 73 81
14. F. Moatar, F. Fessant, and A. Poirel pH modeling by neural networks. Application of control and validation data series in Middle Loire River Ecol. Modell. 120 1999 141 156
15. M.H. Nour, D.W. Smith, M. Gamal El-Din, and E.E. S Prepas Modeling the impact of snowmelt on TP concentration of forested watersheds Northern Alberta: implications to the applicability of time series and artificial neural networks Cold Regions Engineering and Construction Conference and Expo 2004 Edmonton, AB 2004
16. E.E. Prepas, J.M. Burke, I.R. Whitson, G. Putz, and D.W. Smith The forest watershed and riparian disturbance (FORWARD) project: hypothesis development for watershed disturbance modeling in the Canadian boreal forest Cold Regions Engineering and Construction Conference and Expo 2004 Edmonton, AB 2004
17. E.E. Prepas, B. Pinel-Alloul, D. Planes, G. Methot, S. Paquet, and S. Reedyk Forest harvest impacts on water quality and aquatic biota on the Boreal Plain: introduction to the TROLS lake program Can. J. Fish. Aquat. Sci. 58 2001 421 436
18. 2O emissions from a temperate grassland ecosystem Ecol. Modell. 175 2004 189 194
19. R.H. Shumway, and D.S. Stoffer Time Series Analysis and its Applications 2000 Springer New York 549 pp
20. Ward Systems NeuroShell 2, Version 3 1996 Ward Systems Group, Inc. Frederick, MD, USA
21. H. Wilson, and F. Recknagel Towards a generic artificial neural network model fordynamic predictions of algal abundance in freshwater lakes Ecol. Modell. 146 2001 69 84
22. C.M. Zealand, D.H. Burn, and S.P. Simonovic Short term streamflow forecasting using artificial neural networks J. Hydrol. 214 1999 32 48
23. Q. Zhang, A.C. Audrey, R. Shariff, and S.J. Stanley Implementing artificial neural network models for real-time water color forecasting in a water treatment plant J. Environ. Eng. Sci. 3 2004 S15 S23
24. Q. Zhang, and S.J. Stanley Forecasting raw water quality parameters for North Saskatchewan River by neural network modeling Water Res. 31 1997 2340 2350