Artificial Neural Network Models of Watershed Nutrient Loading

Water Resources Management

Volumn 26, Issue 10, 2012, Pages 2781-2797

  Kim, Raymond J ^a   Loucks, Daniel P ^a   Stedinger, Jery R ^a  

^a Cornell University   (United States)

Author keywords

Artificial Neural Networks; Feed forward network; GWLF; Runoff; SWAT; Watershed model; West Basin Delaware River watershed

Indexed keywords

DELAWARE RIVER; FEED-FORWARD NETWORK; GWLF; SWAT; WATERSHED MODELS;

LOADING; NEURAL NETWORKS; RUNOFF; WATER MANAGEMENT; WATERSHEDS;

LANDFORMS;

ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; NUMERICAL MODEL; NUTRIENT; WATER MANAGEMENT; WATERSHED;

DELAWARE RIVER; UNITED STATES;

EID: 84862619383     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-012-0045-x     Document Type: Article

References (36)

1. Aguilera P (2001) Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality. Water Res 35(17): 4053-4062.
2. Altunkaynak A (2007) Forecasting surface water level fluctuations of Lake van by artificial neural networks. Water Resour Manage 21(2): 399-408.
3. Arnold J, Williams J, Srinivasan R, King K (1996) SWAT: soil and water assessment tool. Temple Texas: USDA-ARS Grassland Soil and Water Research Laboratory.
4. Blum A (1992) Neural networks in C++: an object-oriented framework for building connectionist systems. John Wiley & Sons Inc.
5. Catalao J et al (2007) Short-term electricity prices forecasting in a competitive market: a neural network approach. Elec Power Syst Res 77(10): 1297-1304.
6. Dibike YB, Solomatlne DP, Section H (2001) River flow forecasting using artificial neural networks. Science 26(1): 1-7.
7. Dogan E, Sengorur B, Koklu R (2009) Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J Environ Manage 90(2): 1229-1235.
8. El-Shafie A, Noureldin A, Taha M (2008) Neural network model for Nile river inflow forecasting based on correlation analysis of historical inflow data. J Appl Sci 8(24): 4487-4499.
9. Fogelman S, Blumenstein M, Zhao H (2006) Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks. Neural Comput Appl 15(3-4): 197-203.
10. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4(1): 1-58.
11. Gopakumar R, Takara K, James EJ (2007) Hydrologic data exploration and river flow forecasting of a humid tropical river basin using artificial neural networks. Water Resour Manage 21(11): 1915-1940.
12. Haith D, Mandel R (2010) Generalized watershed loading functions user's manual version 3. 0. Department of Agricultural and Biological Engineering, Cornell University, New York, pp. 1-57.
13. Haith D, Shoemaker L (1987) Generalized watershed loading functions for stream flow nutrients. Water Resour Bull 23(3).
14. Hajnayeb A, Ghasemloonia A, Khadem S, Moradi M (2011) Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Exp Syst Appl v38 n8 (201108), 10205-10209.
15. Hecht-Nielsen R (1988) Theory of the backpropagation neural network. In: Neural Networks, 1989. IJCNN., International Joint Conference on. IEEE, pp. 593-605.
16. Howarth RW, Sharpley A, Walker D (2002) Sources of nutrient pollution to coastal waters in the United States: implications for achieving coastal water quality goals. Estuaries 25(4): 656-676.
17. Iliadis LS, Maris F (2007) An artificial neural network model for mountainous water-resources management: the case of Cyprus mountainous watersheds. Environ Model Software 22(7): 1066-1072.
18. Johnes P (1996) Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modelling approach. J Hydrol 183(3-4): 323-349.
19. Kim M, Gilley J (2008) Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput Electron Agric 64(2): 268-275.
20. Lek S, Gue JF (1999) Artificial neural networks as a tool in ecological modelling, an introduction. J Ecol Model 120: 65-73.
21. Lobbrecht A, Solomatine D (1999) Control of water levels in polder areas using neural networks and fuzzy adaptive systems. Water Ind Syst Model Optim Appl 1: 509-518.
22. Loucks D, van Beek E (2005) Water resources systems planning and management (studies and reports in hydrology). United Nations Educational and Scientific Organization, Paris, pp 147-167.
23. Maier H (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Software 19(5): 485-494.
24. Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41(3): 399-417.
25. Mohanty S et al (2010) Artificial neural network modeling for groundwater level forecasting in a River Island of Eastern India. Water Resour Manage 24(9): 1845-1865.
26. Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manage 20(1): 77-90.
27. Neitsch SL, Arnold J (2010) Soil and water assessment tool input/output file documentation, Version 2009. Temple, TX: Blackland Research Center, USDA Agricultural Research Service.
28. New York City Watershed (2006) New York City Watershed Section 319 National Monitoring Program Project. New York City Watershed, Section 31, pp. 208-230.
29. Schalkoff R (1997) Artificial neural networks. The McGraw-Hill Companies, Inc., New York.
30. Soroush AR, Kamal-Abadi N (2009) Review on applications of artificial neural networks in supply chain management and its future. World Appl Sci J 6: 12-18.
31. Suen JP, Ehart J (2003) Evaluation of neural networks for modeling nitrate concentrations in rivers. J Water Resour Plann Manage 129: 505.
32. Swingler K (1996) Applying neural networks: a practical guide. Academic, London.
33. Tarassenko L (1998) A guide to neural computing applications. Arnold Publishers, London.
34. Tayfur G, Swiatek D (2005) Case study: finite element method and artificial neural network models for flow through Jeziorsko Earthfill Dam in Poland. J Hydraul Eng 131(6): 431.
35. US EPA (2007) Total maximum daily loads: nNational section 303(d) list fact sheet. U. S. Environmental Protection Agency, Washington.
36. Zaheer I, Bai C (2003) Application of artificial neural network for water quality management. Lowl Technol Int 5(2): 10-15.