Basin scale water management and forecasting using artificial neural networks

Journal of the American Water Resources Association

Volumn 41, Issue 1, 2005, Pages 195-208

  Khalil, Abedalrazq F ^a   McKee, Mac ^a   Kemblowski, Mariush ^a   Asefa, Tirusew ^a  

^a Utah State University   (United States)

Author keywords

Artificial neural networks; Irrigation; Multi sensor data; Multi time scale forecasting; Streamflow; Water management

Indexed keywords

ARID REGIONS; DECISION MAKING; FORECASTING; IRRIGATION CANALS; NEURAL NETWORKS; RESERVOIRS (WATER); STREAM FLOW; WATER RESOURCES;

FARMERS; SURFACE TEMPERATURE; WATER MANAGEMENT;

CATCHMENTS;

ARTIFICIAL NEURAL NETWORK; BASIN MANAGEMENT; FORECASTING METHOD; IRRIGATION; RESOURCE SCARCITY; WATER MANAGEMENT; WATER RESOURCE;

NORTH AMERICA; SEVIER COUNTY [UTAH]; UNITED STATES; UTAH; WESTERN HEMISPHERE; WORLD;

EID: 17244377563     PISSN: 1093474X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1752-1688.2005.tb03728.x     Document Type: Article

References (22)

1. Ames, D., 1998. Seasonal to Interannual Streamflow Forecasts Using Nonlinear Timeseries Methods and Climate Information. Master of Science Thesis, Utah State University, Logan, Utah.
2. Berger, B., R. Hansen, and A. Hilton, 2002. Using the World-Wide-Web as a Support System to Enhance Water Management. The 18th ICID Congress and 53rd IEC Meeting, Montréal, Canada, pp. 25-1 to 25-12.
3. Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press.
4. Slum, A., A. Kalai, and J. Langford, 1999. Beating the Holdout: Bounds for k-Fold and Progressive Cross-Validation. Proceedings of the 12th Annual Conference on Computational Learning Theory, pp. 203-208.
5. Bret, B., H. Rogers, and R. Jensen, 2002. Sevier River Basin System Description. Available at http://www.sevierriver.org/sys_desc/t1.html. Accessed on April 20, 2004.
6. David, R.L. and M.J. Gregory, 1999. Evaluating the Use of "Goodness-of-Fit" Measures in Hydrologic and Hydroclimatic Model Validation. Water Resources Research 35(1):233-241.
7. Govindaraju, R.S. and A.R. Rao, 2000. Artificial Neural Networks in Hydrology. Kluwer Academic Publishers, Amsterdam, The Netherlands.
8. Hammerstrom, D., 1993. Working With Neural Networks. IEEE Spectrum, July, pp. 46-53.
9. Hayken, S., 1994. Neural Networks: A Comprehensive Foundation. IEEE Press, McMillan College Publishing, New York, New York.
10. Kaplan, A., M. Cane, Y. Kushnir, A. Clement, M. Blumenthal, and B. Rajagopalan, 1998. Analyses of Global Sea Surface Temperature 1856-1991. Journal of Geophysical Research 103:18,567-18,589.
11. Kaplan, A., Y. Kushnir, M. Cane, and M. Blumenthal, 1997. Reduced Space Optimal Analysis for Historical Datasets: 136 Years of Atlantic Sea Surface Temperatures. Journal of Geophysical Research 102:27,835-27,860.
12. Maier, H.R. and G.C. Dandy, 2000. Neural Networks for the Prediction and Forecasting of Water Resources Variables: A Review of Modeling Issues and Applications. Environmental Modeling and Software 15:101-124.
13. Nabney, I., 2001. Netlab: Algorithms for Pattern Recognition. Springer, New York, New York.
14. NeuralWare, Inc., 2000. Neural Computing, NeuralWorks Professional II/PLUS. Carnegie, Pennsylvania.
15. Rivals, I. and L. Personnaz, 2000. A Statistical Procedure for Determining the Optimal Number of Hidden Neurons of a Neural Model. Second International Symposium on Neural Computation, Berlin, Germany.
16. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, 1986. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D.E. Rumelhart and J.L. McClelland (Editors). MIT Press, Cambridge, Massachusetts, Vol. 1, Chapter 8, pp. 318-362.
17. Schalkoff, R.J., 1997. Artificial Neural Networks. McGraw-Hill, New York, New York.
18. Sevier River Water Users Association, 2004. Sevier River Water Users Association: Real-time Water/Weather Data. Available at http://www.sevierriver. org/. Accessed in December 08, 2004.
19. Shakhnarovich, G., R. El-Yaniv, and Y Baram, 2001. Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. Proceedings of International Conference on Machine Learning, pp. 521-528.
20. Skapura, D.M., 1995. Building Neural Networks. Addison-Wesley Publishing Company, Boston, Massachusetts.
21. Sudheer K.P., P.C. Nayak, and K.S. Ramasastri, 2003. Improving Peak Flow Estimates in Artificial Neural Network River Flow Models. Hydrological Processes 17:677-686.
22. Utah Board of Water Resources, 2001. Utah's Water Resources Planning for the Future. Division of Water Resources Publications, Salt Lake City,Utah. Available at http://www.water.utah.gov/waterplan/uwrpff/TOC.htm. Accessed on May 21, 2001.