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Modelling and Simulation in Engineering
Volume 2014, Article ID 635018, 10 pages
http://dx.doi.org/10.1155/2014/635018
Research Article

An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

Department of Hydraulic and Hydrology, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 Johor, Johor Bahru, Malaysia

Received 28 May 2013; Revised 9 November 2013; Accepted 20 November 2013; Published 6 February 2014

Academic Editor: Dariusz J. Gawin

Copyright © 2014 Milad Jajarmizadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. L. Dingman, Physical Hydrology, Prentice Hall, Upper Saddle River, NJ, USA, 2nd edition, 2002.
  2. M. Jajarmizadeh and M. Sobri Harun Salarpour, “A review on theoretical consideration and types of models in hydrology,” Journal of Environmental Science and Technology, vol. 5, no. 5, pp. 249–261, 2012. View at Google Scholar
  3. E. M. Shaw, Hydrology in Practice, Chapman and Hall, London, UK, 1994.
  4. D. Anderson and G. Mcneill, “Artificial Neural Networks Technology,” Tech. Rep. RL/C3C, Rom laboratory, Griffiss AFB, Rome, NY, USA.
  5. ASCE, “Artificial neural networks in hydrology. I: preliminary concepts,” Journal of Hydrologic Engineering, vol. 5, no. 2, pp. 115–123, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. A. R. Senthil Kumar, K. P. Sudheer, S. K. Jain, and P. K. Agarwal, “Rainfall-runoff modelling using artificial neural networks: comparison of network types,” Hydrological Processes, vol. 19, no. 6, pp. 1277–1291, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. G. J. Bowden, G. C. Dandy, and H. R. Maier, “Input determination for neural network models in water resources applications. Part 1—background and methodology,” Journal of Hydrology, vol. 301, no. 1–4, pp. 75–92, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. A. M. Kalteh, P. Hjorth, and R. Berndtsson, “Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application,” Environmental Modelling and Software, vol. 23, no. 7, pp. 835–845, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Salarpour, Z. Yusop, M. Jajarmizadeh, and F. Yusof, “Annual peak flow prediction by multi layer perceptron network in tropical region: johor watershed,” in Proceeding of the 2nd International Conference on Water Resources (ICWR '12), Langkawi, Malaysia, 2012.
  10. M. Salarpour, Z. Yusop, M. Jajarmizadeh, S. Shahid, and F. Yusof, “A generalized feed forward network for the prediction of runoff in Johor River Basin,” in Proceeding of the 4th International Graduate Conference on Engineering Science & Humanity (IGCESH '13), pp. 16–17, Johor, Malaysia, 2013.
  11. E. K. Lafdani, A. Moghaddam Nia, C. A. Pahlavanravi A, M. Ahmadi, and Jajarmizadeh, “Daily rainfall-runoff prediction and simulation using ann, anfis and conceptual hydrological MIKE11/NAM models,” International Journal of Engineering & Technology Sciences, vol. 1, no. 1, pp. 32–50, 2013. View at Google Scholar
  12. E. Kakaei Lafdani, A. R. M. Nia, M. Jajarmizadeh, A. Ahmadi, and M. G. Gosheh, “Stream flow simulation using SVM, ANFIS and NAM models,” Caspian Journal of Applied Sciences Research, vol. 2, no. 4, pp. 86–93.
  13. H. R. Maier and G. C. Dandy, “Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications,” Environmental Modelling and Software, vol. 15, no. 1, pp. 101–124, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Wu, “Prediction of Rainfall Time Series Using Modular RBF Neural Network Model Coupled With SSA and PLS,” in Intelligent Information and Database Systems, vol. 7197, pp. 509–518, Springer, Berlin, Germany, 2012. View at Google Scholar
  15. C. L. Wu and K. W. Chau, “Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis,” Journal of Hydrology, vol. 399, no. 3-4, pp. 394–409, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. R. P. Deshmukh and A. A. Ghatol, “Modular neural network approach for short term flood forecasting a comparative study,” International Journal of Advanced Computer Science and Applications, vol. 1, no. 50, pp. 81–87, 2010. View at Google Scholar
  17. G. Corzo and D. Solomatine, “Baseflow separation techniques for modular artificial neural network modelling in flow forecasting,” Hydrological Sciences Journal, vol. 52, no. 3, pp. 491–507, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Parasuraman, A. Elshorbagy, and S. K. Carey, “Spiking modular neural networks: a neural network modeling approach for hydrological processes,” Water Resources Research, vol. 42, no. 5, Article ID W05412, pp. 1–14, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Jin, J. Jin, and C. Yao, “A short-term climate prediction model based on a modular fuzzy neural network,” Advances in Atmospheric Sciences, vol. 22, no. 3, pp. 428–435, 2005. View at Google Scholar · View at Scopus
  20. M. N. Almasri and J. J. Kaluarachchi, “Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data,” Environmental Modelling and Software, vol. 20, no. 7, pp. 851–871, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Zhang and R. S. Govindaraju, “Prediction of watershed runoff using Bayesian concepts and modular neural networks,” Water Resources Research, vol. 36, no. 3, pp. 753–762, 2000. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Wang, P. H. A. J. M. van Gelder, J. K. Vrijling, and J. Ma, “Forecasting daily streamflow using hybrid ANN models,” Journal of Hydrology, vol. 324, no. 1–4, pp. 383–399, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. E. Yakhkeshi, Application of artificial neural network models in flood warning system preparation [Ph.D. dissertation], Azad University, Tehran, Iran, 2007.
  24. A. S. Tokar and P. A. Johnson, “Rainfall-runoff modeling using artificial neural networks,” Journal of Hydrologic Engineering, vol. 4, no. 3, pp. 232–239, 1999. View at Publisher · View at Google Scholar · View at Scopus
  25. P. O. Yapo, H. V. Gupta, and S. Sorooshian, “Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data,” Journal of Hydrology, vol. 181, no. 1–4, pp. 23–48, 1996. View at Google Scholar · View at Scopus
  26. V. Nourani and O. Kalantari, “Integrated artificial neural network for spatiotemporal modeling of rainfall-runoff-sediment processes,” Environmental Engineering Science, vol. 27, no. 5, pp. 411–422, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. M. H. Beale, M. T. Hagan, and H. B. Demuth, Neural Network Toolbox, Users Guide, vol. 7, The Math Works, 2010.
  28. S. Riad, J. Mania, L. Bouchaou, and Y. Najjar, “Predicting catchment flow in a semi-arid region via an artificial neural network technique,” Hydrological Processes, vol. 18, no. 13, pp. 2387–2393, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Bhadra, A. Bandyopadhyay, R. Singh, and N. S. Raghuwanshi, “Rainfall-runoff modeling: comparison of two approaches with different data requirements,” Water Resources Management, vol. 24, no. 1, pp. 37–62, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. M. R. Zadeh, S. Amin, D. Khalili, and V. P. Singh, “Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions,” Water Resources Management, vol. 24, no. 11, pp. 2673–2688, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. B. Sivakumar, A. W. Jayawardena, and T. M. K. G. Fernando, “River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches,” Journal of Hydrology, vol. 265, no. 1–4, pp. 225–245, 2002. View at Publisher · View at Google Scholar · View at Scopus
  32. N. Sajikumar and B. S. Thandaveswara, “A non-linear rainfall-runoff model using an artificial neural network,” Journal of Hydrology, vol. 216, no. 1-2, pp. 32–55, 1999. View at Publisher · View at Google Scholar · View at Scopus
  33. World Meteorological Organistaion, “Inter-comparison of conceptual models used in operational hydrological forecasting,” Tech. Rep. 429, World Meteorological Organization, Geneva, Switzerland, 1975. View at Google Scholar
  34. P. Srivastava, J. N. McNair, 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, no. 3, pp. 545–563, 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. P. Kneale, L. See, and A. Smith, “Towards defining evaluation measures for neural network forecasting models,” in Proceedings of The 6th International Conference on Geocomputation, Geo Computation, University of QueenslandBrisbane, Australia, 2001.
  36. Z. X. Xu, J. P. Pang, C. M. Liu, and J. Y. Li, “Assessment of runoff and sediment yield in the miyun reservoir catchment by using SWAT model,” Hydrological Processes, vol. 23, no. 25, pp. 3619–3630, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Tombul and E. Oǧul, “Modeling of rainfall-runoff relationship at the semi-arid small catchments using artificial neural networks,” in Intelligent Control and Automation, vol. 344 of Lecture Notes in Control and Information Sciences, pp. 309–318, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. A. R. Ghumman, Y. M. Ghazaw, A. R. Sohail, and K. Watanabe, “Runoff forecasting by artificial neural network and conventional model,” Alexandria Engineering Journal, vol. 50, no. 4, pp. 345–350, 2011. View at Publisher · View at Google Scholar · View at Scopus