Neural-Network Models of Rainfall-Runoff Process
Publication: Journal of Water Resources Planning and Management
Volume 121, Issue 6
Abstract
Spatially distributed rainfall patterns can now be detected using a variety of remote-sensing techniques ranging from weather radar to various satellite-based sensors. Conversion of the remote-sensed signal into rainfall rates, and hence into runoff for a given river basin, is a complex and difficult process using traditional approaches. Neural-network models hold the possibility of circumventing these difficulties by training the network to map rainfall patterns into various measures of runoff that may be of interest. To investigate the potential of this approach, a very simple 5 × 5 grid cell synthetic watershed is used to generate runoff from stochastically generated rainfall patterns. A backpropagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern. Additionally, the neural network is trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.
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References
1.
Bras, R. L. (1990). Hydrology, an introduction to hydrologic science, 1st Ed., Addison-Wesley Publishing Co., Reading, Mass.
2.
Chen, C. H., ed. (1991). Neural networks in pattern recognition and their applications, World Scientific, Singapore.
3.
Cheng, K. S., and Shih, S. F.(1992). “Rainfall area identification using GOES satellite data.”J. Irrig. and Drain. Engrg., 118(1), 179–190.
4.
Dayhoff, J. (1990). Neural network architectures, an introduction, Van Nostrand Reinhold, New York, N.Y.
5.
Duchon, C. E., Salisbury, J. M., Williams, T. H., and Nicks, A. D.(1992). “An example of using Landsat and GOES Data in a Water Budget Model.”Water Resour. Res., 28(2), 527–538.
6.
Hirose, Y., Yamashita, K., and Hijiya, S.(1991). “Back-propagation algorithm which varies the number of hidden units.”Neural Networks, 4, 61–66.
7.
James, W. P., Robinson, C. G., and Bell, J. F.(1993). “Radar-assisted real-time flood forecasting.”J. Water Resour. Plng. and Mgmt., ASCE, 119(1), 32–44.
8.
Karnin, E. D.(1990). “A simple procedure for pruning back-propagation trained neural networks.”IEEE Trans. on Neural Networks, 1(2), 239–242.
9.
Kite, G. W.(1991). “A watershed model using satellite data applied to a mountain basin in Canada.”J. Hydrol., 128, 157–169.
10.
Lippman, R.(1987). “An introduction to computing with neural nets.”IEEE ASSP Mag., 4, 4–22.
11.
Mathews, J. H. (1992). Numerical methods for mathematics, science, and engineering, 2nd Ed., Prentice Hall, Englewood Cliffs, N.J.
12.
McCuen, R. H., and Snyder, W. M. (1986). Hydrologic modeling, statistical methods and applications, 1st Ed., Prentice Hall, Englewood Cliffs, N.J.
13.
Minai, A. A., and Williams, R. D. (1990). “Acceleration of back-propagation through learning rate and momentum adaptation.”Int. Joint Conf. Neural Networks. Lawrence Erlbaum Assoc., Hillsdale, N.J., Vol. 1, 676–679.
14.
Mirchandani, G., and Cao, W.(1989). “On hidden nodes for neural nets,”IEEE Trans. of Circuits and Systems, 36(5), 661–664.
15.
Ponce, V. M. (1989). Engineering hydrology, principles and practices, 1st Ed., Prentice Hall, Inc., Englewood Cliffs, N.J. First edition.
16.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representation by error propagation.”Parallel distributed processing, Vol. 1: foundations, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass.
17.
Simpson, P. (1990). Artificial neural systems, Pergamon Press, New York, N.Y.
18.
Singh, V. P. (1988a). “Rainfall-Runoff Modeling, Vol. 1.”Hydrologic Systems, 1st Ed., Prentice Hall, Englewood Cliffs, N.J.
19.
Singh, V. P. (1988b). “Watershed Modeling, Vol. 2.”Hydrologic Systems, 1st Ed., Prentice Hall, Englewood Cliffs, N.J.
20.
Smith, J. (1992). “Streamflow forecasting using a backpropagation neural network,” M.S. thesis, West Virginia Univ., Morgantown, W.Va.
21.
Spencer, R. W., Goodman, H. M., and Hood, R. E.(1989). “Precipitation retrieval over land and ocean with the SSM/I: identification and characteristics of the scattering signal.”J. Atmospheric and Ocean Technol., 6(2), 254–273.
22.
Viessman, W., Lewis, G. L., and Knapp, J. W. (1989). Introduction to hydrology, 3rd Ed., Harper and Row, Publishers, Inc.
23.
Widrow, B. (1989). “Current and past applications of neural networks to adaptive control problems.”Invited Lecture, West Virginia Univ., College of Engineering, Sept. 27, 1989.
24.
Zurada, J. M. (1992). Introduction to artificial neural systems . West Publishing Co., St. Paul, Minn.
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Copyright © 1995 American Society of Civil Engineers.
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Published online: Nov 1, 1995
Published in print: Nov 1995
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