TECHNICAL PAPERS
Apr 1, 2000

Artificial Neural Networks in Hydrology. I: Preliminary Concepts

Publication: Journal of Hydrologic Engineering
Volume 5, Issue 2

Abstract

In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic appearing in hydrology journals, especially over the last decade. In terms of hydrologic applications, this modeling tool is still in its nascent stages. The practicing hydrologic community is just becoming aware of the potential of ANNs as an alternative modeling tool. This paper is intended to serve as an introduction to ANNs for hydrologists. Apart from descriptions of various aspects of ANNs and some guidelines on their usage, this paper offers a brief comparison of the nature of ANNs and other modeling philosophies in hydrology. A discussion on the strengths and limitations of ANNs brings out the similarities they have with other modeling approaches, such as the physical model.

Get full access to this article

View all available purchase options and get full access to this article.

References

1.
Agyepong, K., and Kothari, R. (1997). “Controlling hidden layer capacity through lateral connections.” Neural Computation, 9(6), 1381–1402.
2.
Almeida, L. B. (1987). “A learning rule for asynchronous perceptrons with feedback in a combinatorial environment.” Proc., IEEE First Int. Conf. on Neural Networks, Institute of Electrical and Electronics Engineers, New York, 2, 609–618.
3.
Almeida, L. B. (1988). “Backpropagation in perceptrons with feedback.” Neural computers, R. Eckmiller and Ch. von der Malsburg, eds., Springer Verlag, Berlin, 199–208.
4.
Barron, A. R. (1993). “Universal approximation bounds for superposition of a sigmoidal function.” IEEE Trans. Information Theory, 39, 930–945.
5.
Baum, E., and Haussler, D. (1989). “What sized net gives valid generalization.” Neural Information Processing Sys., 1, 81–90.
6.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press, New York.
7.
Blackie, J. R., and Eeles, W. O. (1985). “Lumped catchment models.” Hydrological forecasting, M. G. Anderson and T. P. Burt, eds., Wiley, New York, 311–345.
8.
Bose, N. K., and Garga, A. K. (1993). “Neural network design using Voronoi diagrams.” IEEE Trans. on Neural Networks, 4(5), 778–787.
9.
Carpenter, W. C., and Barthelemy, J. (1994). “Common misconcepts about neural networks as approximators.”J. Comp. in Civ. Engrg., ASCE, 8(3), 345–358.
10.
Caudill, M. (1987). “Neural networks primer I.” AI Expert.
11.
Caudill, M. (1988). “Neural networks primer II, III, IV and V.” AI Expert.
12.
Caudill, M. (1989). “Neural networks primer VI, VII and VIII.” AI Expert.
13.
Cios, K. J., and Liu, N. (1992). “A machine leaning method for generation of a neural network architecture: a continuous ID3 algorithm.” IEEE Trans. on Neural Networks, 3(2), 280–291.
14.
Corradini, C., and Singh, V. P. (1985). “Effect of spatial variability of effective rainfall on direct runoff by a geomorphological approach.” J. Hydrol., Amsterdam, 81, 27–43.
15.
Elman, J. L. (1990). “Finding structure in time.” Cognitive Sci., 14, 179–211.
16.
Fahlman, S. E., and Lebiere, C. (1990). “The cascade-correlation learning architecture.” Advances in neural information processing systems 2, D. S. Touretzky, ed., Morgan Kaufmann, San Mateo, Calif., 524–532.
17.
Fahlman, S. E., and Lebiere, C. (1991). “The cascade-correlation learning architecture.” CMU Tech. Rep. CMU-CS-90-100, Carnegie Mellon University, Pittsburgh, Pa.
18.
Fausett, L. (1994). Fundamentals of neural networks. Prentice Hall, Englewood Cliffs, N.J.
19.
Fletcher, R., and Reeves, C. M. (1964). “Function minimization by conjugate gradient.” Comp. J., 7, 149–154.
20.
Freeze, R. A., and Harlan, R. L. (1969). “Blueprint for a physically-based digital simulated hydrologic response model.” J. Hydrol., Amsterdam, 9, 237–258.
21.
French, M. N., Krajewski, W. F., and Cuykendal, R. R. (1992). “Rainfall forecasting in space and time using a neural network.” J. Hydrol., Amsterdam, 137, 1–37.
22.
Gallant, S. I. (1986). “Three constructive algorithms for network learning.” Proc., 8th Ann. Conf. of the Cognitive Sci. Soc., Cognitive Science Society, Ann Arbor, Mich., 652–660.
23.
Gupta, V. K., and Waymire, E. (1983). “On the formulation of an analytical approach to hydrologic response and similarity at the basin scale.” J. Hydrol., Amsterdam, 65, 95–123.
24.
Hassibi, B., and Stork, D. G. (1993). “Second order derivatives for networks pruning: optimal brain surgeon.” Proc., Neural Information Processing Sys. 4, MIT Press, Cambridge, Mass., 164–171.
25.
Haykin, S. (1994). Neural networks: a comprehensive foundation. MacMillan, New York.
26.
Hertz, J., and Palmer, R. G. (1991). Introduction to the theory of neural computation. Addison-Wesley, Reading, Mass.
27.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities.” Proc., Nat. Academy of Scientists, 79, 2554–2558.
28.
Islam, S., and Kothari, R. (2000). “Artificial neural networks in remote sensing of hydrologic processes.”J. Hydrologic Engrg., ASCE, 5(2), 138–144.
29.
Karnin, E. D. (1990). “A simple procedure for pruning back propagation trained neural networks.” IEEE Trans. Neural Networks, 1, 239–242.
30.
Kohonen, T. (1989). Self organization and associative memory. Springer Verlag, New York.
31.
Kohonen, T. (1990). “The self organizing map.” Proc. IEEE, 78, 1464–1480.
32.
Kothari, R., and Agyepong, K. (1996). “On lateral connections in feed-forward neural networks.” Proc., IEEE Int. Conf. on Neural Networks, Institute of Electrical and Electronics Engineers, New York, 13–18.
33.
Kothari, R., and Agyepong, K. (1997). “Induced specialization of context units for temporal pattern recognition and reproduction.” Proc., IEEE Neural Networks for signal Processing VII, J. Principe, L. Gile, N. Morgan, and E. Wilson, eds., Institute of Electrical and Electronics Engineers, New York, 131–140.
34.
Kwok, T., and Yeung, D. (1995). “Constructive feedforward neural networks for regression problems: a survey.” Tech. Rep., Hong Kong University of Science & Technology, Hong Kong.
35.
LeCun, Y., Denker, J. S., and Solla, S. A. (1990). “Optimal brain damage.” Proc., Neural Information Processing Sys. 2, MIT Press, Cambridge, Mass., 598–605.
36.
Leonard J. A., Kramer, M. A., and Ungar, L. H. (1992). “Using radial basis functions to approximate a function and its error bounds.” IEEE Trans. on Neural Networks, 3(4), 624–627.
37.
Maier, H. R., and Dandy, G. C. (1996). “The use of artificial neural networks for the prediction of water quality parameters.” Water Resour. Res., 32(4), 1013–1022.
38.
McCuen, R. H. (1997). Hydrologic analysis and design, 2nd Ed., Prentice Hall, Upper Saddle River, N.J.
39.
McCulloch, W. S., and Pitts, W. (1943). “A logic calculus of the ideas immanent in nervous activity.” Bull. of Math. Biophys., 5, 115–133.
40.
Michaud, J., and Sorooshian, S. (1994). “Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed.” Water Resour. Res., 30(3), 593–605.
41.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall-runoff models.” Hydrological Sci., 41(3), 399–417.
42.
Mozer, M. C., and Smolensky, P. (1989). “Skeletonization: a technique for trimming the fat from a network via relevance assessment.” Advances in neural information processing systems 1, D. Touretzky, ed., Morgan Kaufmann, San Monteo, Calif., 107–115.
43.
Nadal, J. P. (1989). “Study of a growth algorithm for neural networks.” Int. J. Neural Sys., 1, 55–59.
44.
Nowlan, S. J., and Hinton, G. E. (1992). “Simplifying neural networks by soft weight sharing.” Neural Computation, 4, 473–493.
45.
Pineda, F. J. (1987). “Generalization of back-propagation to recurrent neural networks.” Phys. Rev. Lett., 59, 2229–2232.
46.
Pineda, F. J. (1989). “Recurrent back-propagation and the dynamical approach to adaptive neural computation.” Neural Computation, 1, 161–172.
47.
Ranjithan, S., Eheart, J. W., and Rarret, Jr., J. H. (1993). “Neural network-screening for groundwater reclamation under uncertainty.” Water Resour. Res., 29(3), 563–574.
48.
Reed, R. (1993). “Pruning algorithm—a survey.” IEEE Trans. Neural Networks, 4, 740–747.
49.
Rogers, 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.
50.
Rohwer, R., and Forrest, B. (1987). “Training time-dependence in neural networks.” Proc., IEEE 1st Int. Conf. on Neural Networks, Institute of Electrical and Electronics Engineers, New York, 2, 701–708.
51.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representations by error propagation.” Parallel distributed processing, Vol. 1, MIT Press, Cambridge, Mass., 318–362.
52.
Smith, J., and Eli, R. N. (1995). “Neural-network models of rainfall-runoff process.”J. Water Resour. Plng. and Mgmt., ASCE, 121(6), 499–508.
53.
Thirumaliah, K., and Deo, M. C. (1998). “River stage forecasting using artificial neural networks.”J. Hydrologic Engrg., ASCE, 3(1), 27–32.
54.
Wasserman, P. D. (1989). Neural computing: theory and practice. Van Nostrand Reinhold, New York.
55.
Werbos, P. (1974). “Beyond regression: new tools for prediction and analysis in the behavioral sciences,” PhD dissertation, Harvard University, Cambridge, Mass.

Information & Authors

Information

Published In

Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 5Issue 2April 2000
Pages: 115 - 123

History

Received: Aug 20, 1998
Published online: Apr 1, 2000
Published in print: Apr 2000

Permissions

Request permissions for this article.

Authors

Affiliations

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology
Rao S. Govindaraju, Assoc. Prof., Purdue Univ., School of Civ. Engrg., 1284 Civ. Engrg. Build., West Lafayette, IN 47907-1284.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share