Discussion of “Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms” by Jan Adamowski and Christina Karapataki
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 7
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References
Andréassian, V., Parent, E., and Michel, C. (2000). “Using a parsimonious rainfall-runoff model to detect non-stationarities in the hydrological behaviour of watersheds.” Hydrol. Sci. J.HSJODN, 45, 537–546.
Dawson, C. W., and Wilby, R. L. (2001). “Hydrological modeling using artificial neural networks.” Prog. Phys. Geogr.PPGEEC, 25(1), 80–108.
Ke, J., and Liu, X. (2008). “Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction.” IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, New York, 828–832.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environ. Modell. SoftwareEMSOFT, 15(1), 101–124.
Perrin, C., Michel, C., and Andréassian, V. (2001). “Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments.” J. Hydrol. (Amsterdam)JHYDA7, 242, 275–301.
Pulido-Calvo, I., Montesinos, P., Roldán, J., and Ruiz-Navarro, F. (2007). “Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems.” Biosystems Eng.BEINBJ, 97(2), 283–293.
Ray, C., and Klindworth, K. K. (2000). “Neural networks for agrichemical vulnerability assessment of rural private wells.” J. Hydrol. Eng.JHYEFF, 5(2), 162–171.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2002). “A data-driven algorithm for constructing artificial neural network rainfall-runoff models.” Hydrol. ProcessesHYPRE3, 16(6), 1325–1330.
Wanas, N., Auda, G., Kamel, M. S., Karray, F. (2002). “On the optimal number of hidden nodes in a neural network.” Proc., IEEE Canadian Conf. on Electrical and Computer Engineering, Vol. 2, IEEE, New York, 918–921.
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© 2012. American Society of Civil Engineers.
History
Received: Jan 21, 2011
Accepted: Apr 19, 2011
Published online: Jun 15, 2012
Published in print: Jul 1, 2012
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