Evaluation of Neural Network Streamflow Forecasting on 47 Watersheds
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 1
Abstract
This study is designed to compare ahead streamflow forecasting performance of multiple-layer perceptron (MLP) networks implemented at a daily time step for 47 watersheds spread across France and Central United States. In order to keep the task to manageable proportions, a large sample of test watersheds asks for a reduction of the number of steps in the network implementation procedure. This is achieved by eliminating the long trial and error process of input selection. Results show that it is feasible to obtain good ahead streamflow forecasting performance from simple MLPs and input vectors consisting solely of the last observed streamflow and a predetermined range of precipitation observations that is roughly equal to the time of concentration of the watersheds. Also, intuitive preprocessing such as differencing the streamflow noticeably improves the forecasting performance in almost all instances. On the other hand, consideration of the potential evapotranspiration as an additional input decreases the MLP’s performance in the majority of instances. Finally, it is noteworthy that there is a general trend between the watershed runoff coefficients and the ability of the MLPs to correctly map ahead streamflows.
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Acknowledgments
This work was performed during the sabbatical leave of the first writer from Université Laval to Cemagref. Support from both institutions is gratefully acknowledged. The MOPEX database was retrieved from ⟨www.ogp.noaa.gov/mpe/gapp/ceop/mopex.htm⟩.
References
American Society of Civil Engineering (ASCE) Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrologic Eng., 5(2), 115–123.
American Society of Civil Engineering (ASCE) Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrologic Eng., 5(2), 124–137.
Anctil, F., Michel, C., Perrin, C., and Andréassian, V., (2004a). “A soil moisture index as an auxiliary ANN input for stream flow forecasting.” J. Hydrol., 286, 155–167.
Anctil, F., Perrin, C., and Andréassian, V., (2004b). “Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models.” Environ. Model. Software, 19, 357–368.
Atiya, A. F., El-Shoura, S. M., Shaheen, S. I., and El-Sherif, M. S. (1999). “A comparison between neural-network forecasting techniques—Case study: River flow forecasting.” IEEE Trans. Neural Netw., 10(2), 402–409.
Birikundavyi, S., Labib, R., Trung, H. T., and Rouselle, J. (2002). “Performance of neural networks in daily streamflow forecasting.” J. Hydrologic Eng., 7(5), 392–398.
Campolo, M., Andreussi, P., and Soldati, A. (1999). “River flood forecasting with a neural network model.” Water Resour. Res., 35, 1191–1197.
Coulibaly, P., Anctil, F., and Bobée, B. (1999). “Prévision hydrologique par réseaux de neurones artificiels: état de l’art.” Can. J. Civ. Eng., 26(3), 293–304.
Coulibaly, P., Anctil, F., and Bobée, B. (2001). “Multivariate reservoir inflow forecasting using temporal neural networks.” J. Hydrologic Eng., 6(5), 367–376.
Dawson, C. W., and Wilby, R. (2001). “Hydrological modelling using artificial neural networks.” Progress Physical Geogr., 25(1), 80–108.
Irvine, K. N., and Eberhardt, A. J. (1992). “Multiplicative seasonal ARIMA models for Lake Erie and Lake Ontario water levels.” Water Resour. Bull., 28(2), 385–396.
Iyer, M. S., and Rhinehart, R. R. (1999). “A method to determine the required number of neural-network training repetitions.” IEEE Trans. Neural Netw., 10(2), 427–432.
Kitadinis, P. K., and Bras, R. L. (1980). “Real-time forecasting with a conceptual hydrologic model: 2. Applications and results.” Water Resour. Res., 16, 1034–1044.
Lauzon, N., Rousselle, J., Birikundavyi, S., and Trung, H. T. (2000). “Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks.” Can. J. Civ. Eng., 27, 671–682.
MacKay, D. J. C. (1992). “Bayesian interpolation.” Neural Comput., 4, 415–447.
Mac Namee, B., Cunningham, P., Byrne, S., and Corrigan, O. I. (2002). “The problem of bias in training data in regression problems in medical decision support.” Artif. Intell. Med., 24, 51–70.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for prediction and forecasting of water resources variables: Review of modelling issues and applications.” Environ. Model. Software, 15, 101–124.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall-runoff models.” Hydrol. Sci. J., 41, 399–417.
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., 242, 275–301.
Salas, J. D., Markus, M., and Tokar, A. S. (2000). “Streamflow forecasting based on artificial neural networks.” Artificial neural networks in hydrology, R. S. Govindaraju and A. Ramachandra Rao, eds., Chap. 2, Kluwer, Dordrecht, The Netherlands.
Tan, Y., and van Cauwenberghe, A. (1999). “Neural-network-based d-step-ahead predictors for nonlinear systems with time delay.” Eng. Applic. Artif. Intell., 12(1), 21–25.
Tingsanchali, T., and Gautam, M. R. (2000). “Application of tank, NAM, ARMA, and neural network models to flood forecasting.” Hydrolog. Process., 14, 2473–2487.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrologic Eng., 4(3), 232–239.
Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). “Short term streamflow forecasting using artificial neural networks.” J. Hydrol., 214, 32–48.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 10 • Issue 1 • January 2005
Pages: 85 - 88
Copyright
© 2005 ASCE.
History
Received: Jun 23, 2003
Accepted: Mar 7, 2004
Published online: Jan 1, 2005
Published in print: Jan 2005
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