Prediction of Discharge with Elman and Cascade Neural Networks

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Prediction of Discharge with Elman and Cascade Neural Networks

Author Affiliations

¹ Department of Civil Engineering, N.I.T., Rourkela, Odisa, INDIA

Res. J. Recent Sci., Volume 2, Issue (ISC-2012), Pages 279-284, February,2 (2013)

Abstract

The soft computing techniques have gained popularity for predictions. Stage discharge studies play a crucial role in planning, design or management of any hydraulic system. Over or under estimation of discharge value causes huge loss of investments, structures and lives. Two neural networks have been studied taking stage discharge data of an Indian river named Brahmani. Performance of each network has been summarized. Accuracy of each network model is based on the percentage of successful predictions on the test sets of each data set. Accuracy is measured via the holdout method as well as through cross validation. The present work suggests the suitability of a neural network as a tool for predicting discharge which will be useful in different field of science and engineering.

References

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[ref1] Braca Giovani, Stage-Discharge relation in open channels: practices and problems, Technical report, FORALPS, (2008)

[ref2] Bhattacharya B. and Solomatine D.P., Application of ANN in stage discharge relationship, Proceedings of 4th International Conference on Hydro Informatics, 1-7, (2000)

[ref3] Goel Arun, ANN based approach for predicting rating curve of an Indian river, ISRN:DOI:10.5102/2011/291370, (2011)

[ref4] Goel A. and Pal M., Stage Discharge modeling using Support Vector Machines, IJE Transactions A: Basics, DOI:10.5829/idosi.ije, 25.01a.01, (2012)

[ref5] Bhattacharya B. and Solomatine D.P., Neural networks and M5 model trees in modeling water level-discharge relationship for an Indian river, ESANN`2003 Proceedings, Belgium, 23-25, April, ISBN 2-930307-03-x, 407-412 (2003)

[ref6] Sudhir K. P. and Jain A., Explaining the internal behavior of artificial neural network river flow models, Hydrol. Process, 118(4) , 833-844 (2004)

[ref7] Hajek M., Neural Networks, Neural Networks.doc, (2005)

[ref8] Sumathi S., Sivanandam S.N. and Deepa S.N., Introduction to Neural Networks using MATLAB, TMH, (2006)

[ref9] Haykin Simon O, Neural Networks- A Comprehensive Foundation, 2nd Ed., Pearson Education, (2006)

[ref10] Guven A., Aytek A. and Md. Azamathulla H., A practical approach to formulate stage discharge relationship in natural rivers, J. Neural Computing and Applications, DOI 10.1007/s00521-012-1011-5, (2012)

[ref11] Srinivasulu S. and Jain A., A comparative analysis of training methods for artificial neural networks rainfall-runoff models, Applied Soft Computing, 6, 295-306 (2006)