Performance Evaluation of Artificial Neural Networks for Runoff Prediction
Publication: Journal of Hydrologic Engineering
Volume 5, Issue 4
Abstract
Spring runoff prediction in the Red River Valley, southern Manitoba, Canada, is an important issue because of the devastating effect of the flood of 1997 in that area. Increasing the accuracy of the prediction process is a practical necessity. This study looks at the artificial neural networks (ANN) technique and compares it to linear and nonlinear regression techniques. The advantages and disadvantages of the three modeling techniques are discussed. To fill the predictive accuracy evaluation gap left by the mean squared error and the mean relative error, a modified statistic, namely, pooled mean squared error, is developed and explained. The aim of this work is to show the applicability of ANN for runoff prediction and to evaluate their performances by comparing them with traditional techniques. In this study, according to the accuracy of results, the ANN models show superiority in most of the cases. However, in some situations, the performance of the other two techniques was comparable.
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References
1.
Anderson, J. A. ( 1995). An introduction to neural networks, MIT Press, Cambridge, Mass.
2.
Bastarache, D., El-Jabi, N., Turkkan, N., and Clair, T. A. ( 1997). “Predicting conductivity and acidity for small streams using neural networks.” Can. J. Civ. Engrg., Ottawa, 24, 1030–1039.
3.
Carpenter, W. C., and Barthelemy, J. F. (1994). “Common misconceptions about neural networks as approximators.”J. Comp. in Civ. Engrg., ASCE, 8(3), 345–358.
4.
Dhar, V., and Stein, R. ( 1997). Intelligent decision support methods, Prentice-Hall, Englewood Cliffs, N.J.
5.
Draper, N. R., and Smith, H. ( 1981). Applied regression analysis, Wiley, New York.
6.
Freeman, J. A., and Skapura, D. M. ( 1991). Neural networks. Algorithms, applications, and programming techniques, Addison-Wesley, Reading, Mass.
7.
French, M. N., Krajewski, W. F., and Cuykendall, R. R. ( 1992). “Rainfall forecasting in space and time using a neural network.” J. Hydro., Amsterdam, 137, 1–31.
8.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. ( 1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
9.
International Joint Commission (IJC). ( 1997). “Red River flooding. Short-term measures.” Interim Rep. of the International Red River Basin Task Force, Ottawa-Washington.
10.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.”J. Comp. in Civ. Engrg., ASCE, 8(2), 201–220.
11.
Maren, A., Harston, C., and Pap, R. ( 1990). Handbook of neural computing applications, Academic, San Diego.
12.
Montgomery, D. C., and Peck, E. A. ( 1992). Introduction to linear regression analysis, Wiley, New York.
13.
Raman, H., and Sunilkumar, N. ( 1995). “Multivariate modeling of water resources time series using artificial neural networks.” Hydrological Sci. J., 40(2), 145–163.
14.
Shamseldin, A. Y. ( 1997). “Application of a neural network technique to rainfall-runoff modeling.” J. Hydro., Amsterdam, 199, 272–294.
15.
Sorooshian, S., Duan, Q., and Gupta, V. K. ( 1993). “Calibration of rainfall-runoff models: Application of global optimization to the Sacramento soil moisture accounting model.” Water Resour. Res., 29(4), 1185–1194.
16.
Sorooshian, S., Gupta, V. K., and Fulton, J. L. ( 1983). “Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: Influence of calibration data variability and length on model credibility.” Water Resour. Res., 19(1), 252–259.
17.
Zealand, C. M., Burn, D. H., and Simonovic, S. P. ( 1999). “Short term streamflow forecasting using artificial neural networks.” J. Hydro., Amsterdam, 214, 32–48.
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Received: Jun 17, 1998
Published online: Oct 1, 2000
Published in print: Oct 2000
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