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Comparative study of temporal neural networks for short term flood forecasting

by A. A. Ghatol, Rahul P. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 12
Year of Publication: 2010
Authors: A. A. Ghatol, Rahul P. Deshmukh
10.5120/960-1337

A. A. Ghatol, Rahul P. Deshmukh . Comparative study of temporal neural networks for short term flood forecasting. International Journal of Computer Applications. 5, 12 ( August 2010), 24-28. DOI=10.5120/960-1337

@article{ 10.5120/960-1337,
author = { A. A. Ghatol, Rahul P. Deshmukh },
title = { Comparative study of temporal neural networks for short term flood forecasting },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 12 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume5/number12/960-1337/ },
doi = { 10.5120/960-1337 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:54:10.035462+05:30
%A A. A. Ghatol
%A Rahul P. Deshmukh
%T Comparative study of temporal neural networks for short term flood forecasting
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 12
%P 24-28
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Jordan and general recurrent neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using general recurrent connections. Methodologies and techniques of the two models are presented in this paper and a comparison of the short term runoff prediction results between them is also conducted. The prediction results of the general recurrent neural network indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that the general recurrent network is more versatile than Jordan model and can be considered as an alternate and practical tool for predicting short term flood flow.

References
  1. P. Srivastava, J. N. McVair, and T. E. Johnson, "Comparison of process-based and artificial neural network approaches for streamflow modeling in an agricultural watershed," Journal of the American Water Resources Association, vol. 42, pp. 545563, Jun 2006.
  2. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, pp. 359-366,1989.
  3. M. C. Demirel, A. Venancio, and E. Kahya, "Flow forecast by SWAT model and ANN in Pracana basin, Portugal," Advances in Engineering Software, vol. 40, pp. 467-473, Jul 2009.
  4. A. S. Tokar and M. Markus, "Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models," Journal of Hydrologic Engineering, vol. 5, pp. 156-161,2000.
  5. S. Q. Zhou, X. Liang, J. Chen, and P. Gong, "An assessment of the VIC-3L hydrological model for the Yangtze River basin based on remote sensing: a case study of the Baohe River basin," Canadian Journal of Remote Sensing, vol. 30, pp. 840-853, Oct 2004.
  6. R. J. Zhao, "The Xinanjiang Model," in Hydrological Forecasting Proceedings Oxford Symposium, lASH, Oxford, 1980 pp. 351-356.
  7. R. J. Zhao, "The Xinanjiang Model Applied in China," Journal of Hydrology, vol. 135, pp. 371-381, Ju11992.
  8. D. Zhang and Z. Wanchang, "Distributed hydrological modeling study with the dynamic water yielding mechanism and RS/GIS techniques," in Proc. of SPIE, 2006, pp. 63591Ml-12.
  9. J. E. Nash and I. V. Sutcliffe, "River flow forecasting through conceptual models," Journal ofHydrology, vol. 273, pp. 282290,1970.
  10. D. Zhang, "Study of Distributed Hydrological Model with the Dynamic Integration of Infiltration Excess and Saturated Excess Water Yielding Mechanism." vol. Doctor Nanjing: Nanjing University, 2006, p. 190.529
  11. E. Kahya and J. A. Dracup, "U.S. Streamflow Patterns in Relation to the EI Nit'lo/Southern Oscillation," Water Resour. Res., vol. 29, pp. 2491-2503 ,1993.
  12. K. J. Beven and M. J. Kirkby, "A physically based variable contributing area model of basin hydrology," Hydrologi cal Science Bulletin, vol. 43, pp. 43-69,1979.
  13. N. J. de Vos, T. H. M. Rientjes, “Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation”, Hydrology and Earth System Sciences, European Geosciences Union, 2005, 9, pp. 111-126.
  14. Holger R. Maier, Graeme C. Dandy, “Neural networks for the perdiction and forecasting of water resources variables: a review of modeling issues and applications”, Environmental Modelling & Software, ELSEVIER, 2000, 15, pp. 101-124.
  15. T. Hu, P. Yuan, etc. “Applications of artificial neural network to hydrology and water resources”, Advances in Water Science, NHRI, 1995, 1, pp. 76-82.
  16. Q. Ju, Z. Hao, etc. “Hydrologic simulations with artificial neural networks”, Proceedings-Third International Conference on Natural Computation, ICNC, 2007, pp. 22-27.
  17. G. WANG, M. ZHOU, etc. “Improved version of BTOPMC model and its application in event-based hydrologic simulations”, Journal of Geographical Sciences, Springer, 2007, 2, pp. 73-84.
  18. K. Beven, M. Kirkby, “A physically based, variable contributing area model of basin hydrology”, Hydrological Sciences Bulletin, Springer, 1979, 1, pp.43-69.
  19. K. Thirumalaiah, and C.D. Makarand, Hydrological Forecasting Using Neural Networks Journal of Hydrologic Engineering. Vol. 5, pp. 180-189, 2000.
  20. G. WANG, M. ZHOU, etc. “Improved version of BTOPMC model and its application in event-based hydrologic simulations”, Journal of Geographical Sciences, Springer, 2007, 2, pp. 73-84.
  21. H. Goto, Y. Hasegawa, and M. Tanaka, “Efficient Scheduling Focusing on the Duality of MPL Representatives,” Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS 07), IEEE Press, Dec. 2007, pp. 57-64.
  22. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, ”Artificial neural networks in hydrology I: preliminary concepts”, Journal of Hydrologic Engineering,5(2),pp.115-123,2000.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural network Forecasting Rainfall Runoff Models