Closure to “comparison of anns and empirical approaches for predicting watershed runoff”

Journal of Water Resources Planning and Management

Volumn 128, Issue 5, 2002, Pages 381-388

  Anmala, J ^a   Zhang, B ^b   Govindaraju, R S ^b  

^a Georgia Institute of Technology   (United States)

^b PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85001555749     PISSN: 07339496     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)0733-9496(2002)128:5(381)     Document Type: Article

References (5)

1. ASCE Task Committee on Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology. II: Hydrologie applications.” J. Hydrologie Eng., 5(2), 124-137.
2. Govindaraju, R. S., and Rao, A. R. (2000). “Artificial neural networks: A passing fad in hydrology?” J. Hydrologie Eng., 5(3), 225-226.
3. 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.
4. McCuen, R. H. (1985). Statistical methods for engineers, Prentice-Hall, Englewood Ciffs, N.J.
5. Zhang, B., and Govindaraju, R. S. (2002). “Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds.” J. Hydrol., in press.