Rainfall-Runoff Modeling Using Artificial Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 4, Issue 3
Abstract
An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff as a function of daily precipitation, temperature, and snowmelt for the Little Patuxent River watershed in Maryland. The sensitivity of the prediction accuracy to the content and length of training data was investigated. The ANN rainfall-runoff model compared favorably with results obtained using existing techniques including statistical regression and a simple conceptual model. The ANN model provides a more systematic approach, reduces the length of calibration data, and shortens the time spent in calibration of the models. At the same time, it represents an improvement upon the prediction accuracy and flexibility of current methods.
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References
1.
Caudill, M. (1987). “Neural networks primer, part I.” AI Expert, December, 46–52.
2.
French, M. N., Krajewski, W. F., and Cuykendall, R. R. (1992). “Rainfall forecasting in Space and Time Using a Neural Network.” J. Hydrol., 137, 1–31.
3.
Gupta, V. K., and Sorooshian, S. (1985). “The relationship between data and the precision of parameter estimates of hydrologic models.” J. Hydrol., 81, 57–77.
4.
Hecht-Nielsen, R. (1990). Neurocomputing. Addison-Wesley Publishing Company, Reading, Mass.
5.
Hsu, K., Gupta, V. H., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
6.
Markus, M. ( 1997). “Application of neural networks in streamflow forecasting,” PhD dissertation, Colorado State University, Fort Collins, Colo.
7.
McCuen R. H., and Snyder, W. M. (1986). Hydrologic modeling: statistical methods and applications. Prentice Hall Englewood Cliffs, N.J.
8.
McCuen, R. H. (1993). Statistical hydrology. Prentice Hall, Englewood Cliffs, N.J.
9.
Poff, L. N., Tokar, A. S., and Johnson P. A. (1996). “Stream hydrological and ecological responses to climatic change assessed with an artificial neural network.” Limnol. and Oceanogr., 41(5), 857–863.
10.
Professional II/PLUS and NeuralWorks Explorer. (1993). Tech. Publ. Group NeuralWare, Pittsburgh, Pa.
11.
Roger, L. L., and Dowla, F. U. (1994). “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling.” Water Resour. Res., 30(2), 457–481.
12.
Rumelhart, D. E., McClelland J. L., and PDP Research Group (1986). Parallel distributed processing. Vol. 1: Foundations. The MIT Press, Cambridge, Mass.
13.
Tokar, A. S. ( 1996). “Rainfall-runoff modeling in an uncertain environment,” PhD dissertation, University of Maryland, College Park, Md.
14.
Trent, R., Molinas, A., and Gagarin, N. (1993a). “An artificial neural network for computing sediment transport.” Proc., Conf. on Hydr. Engrg., ASCE, Reston, Va., 1049–1054.
15.
Trent, R., Rhodes, J., and Gagarin, N. (1993b). “Estimating pier scour with artificial neural networks.” Proc., Conf. on Hydr. Engrg., ASCE, Reston, Va., 1043–1048.
16.
Wasserman, P. D. (1989). Neural computing theory and practice. Van Nostrand Reinhold, New York.
17.
Yapo, P. O., Gupta, V. H., and Sorooshian, S. (1996). “Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data.” J. Hydrol., 181, 23–48.
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Published In
Journal of Hydrologic Engineering
Volume 4 • Issue 3 • July 1999
Pages: 232 - 239
History
Published online: Jul 1, 1999
Published in print: Jul 1999
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