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Aug 15, 2002
Closure to “Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff” by Jagadeesh Anmala, Bin Zhang, and Rao S. Govindaraju
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VIEW THE ORIGINAL ARTICLEAuthors: J. Anmala, B. Zhang, and R. S. GovindarajuAuthor Affiliations
Publication: Journal of Water Resources Planning and Management
Volume 128, Issue 5
0733-9496(2002)128:5(381)/asset/5cd7567b-d0ed-4d88-8546-b1d3e59f6cd1/assets/(asce)0733-9496(2002)128:5(381).fp.png)
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References
ASCE Task Committee on Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrologic Eng., 5(2), 124–137.
Govindaraju, R. S., and Rao, A. R.(2000). “Artificial neural networks: A passing fad in hydrology?” J. Hydrologic Eng., 5(3), 225–226.
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 applications.” Environmental Modelling & Software, 15, 101–124.
McCuen, R. H. (1985). Statistical methods for engineers, Prentice-Hall, Englewood Ciffs, N.J.
Zhang, B., and Govindaraju, R. S. (2002). “Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds.” J. Hydrol., in press.
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Journal of Water Resources Planning and Management
Volume 128 • Issue 5 • September 2002
Pages: 381 - 382
History
Published online: Aug 15, 2002
Published in print: Sep 2002
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Authors
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J. Anmala
Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332.
B. Zhang
School of Civil Engineering, Purdue Univ., West Lafayette, IN 47907.
R. S. Govindaraju
Professor, School of Civil Engineering, Purdue Univ., West Lafayette, IN, 47907.
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