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Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels Cover

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Journal of Hydrology and Hydromechanics
Volume 64 (2016): Issue 3 (September 2016)
By: ,  ,  ,   and    
Open Access
|Jul 2016
References

References

  1. Ab Ghani, A., Salem, A.M., Abdullah, R., Yahaya, A.S., Zakaria, N.A., 1999. Incipient motion of sediment particles over loose deposited beds in a rigid rectangular channel. Proc. 8th Int. Conf. Urban Storm Drainage, Sydney, Australia.
    Search in Google Scholar
  2. Ab Ghani, A., Zakaria, N.A., Kassim, M., Nasir, B.A., 2001. Sediment size characteristics of urban drains in Malaysian cities. Urban Water J., 2, 335-341.
    Open DOISearch in Google Scholar
  3. Ab Ghani, A., Azamathulla, H.M., Chang, C.K., Zakaria, N.A., Hasan, Z.A., 2011. Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers. Environ. Fluid Mech., 11, 3, 307-318.
    Open DOISearch in Google Scholar
  4. Ackers, P., White, W.R., 1973. Sediment transport: new approach and analysis. J. Hydraul. Div., 99, 2041-2060.
    Open DOISearch in Google Scholar
  5. Ahmad, Z., Azamathulla, H.Md., Zakaria, N.A., 2011. ANFISbased approach for the estimation of transverse mixing coefficient. Water Sci. Technol., 63, 1005-1010.
    Open DOISearch in Google Scholar
  6. Azamathulla, H.M., Ab Ghani, A., Zakaria, N.A., Kiat, C.C., Siang, L.C., 2008. Knowledge extraction from trained neural network scour models. Modern Appl. Sci., 2, 4, 52-62.
    Open DOISearch in Google Scholar
  7. Bilhan, O., Emiroglu, M.E., Kisi, O., 2010. Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel. Adv. Eng. Softw., 41, 831-837.
    Open DOISearch in Google Scholar
  8. Bonakdari, H., Ebtehaj, I., 2014. Study of sediment transport using soft computing technique. In: Schleiss et al. (Eds): River Flow 2014, Chapter 116, pp. 933-940, Taylor & Francis Group, London, UK.
    Open DOISearch in Google Scholar
  9. Bong, C.H.J., Lau, T.L., Ab Ghani, A., 2013. Verification of equations for incipient motion studies for rigid rectangular channel. Water Sci. Technol., 67, 395-403.
    Open DOISearch in Google Scholar
  10. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1993. Classification and Regression Trees. Wadsworth, Inc. California, USA, 368 p.
    Search in Google Scholar
  11. Christopher, M., 1995. Neural networks for pattern recognition. Oxford University Press, Oxford, UK, 482 p.
    Search in Google Scholar
  12. Ebtehaj, I., Bonakdari, H., 2013. Evaluation of sediment transport in sewer using artificial neural network. Eng. Appl. Comput. Fluid Mech., 7, 382-392.
    Open DOISearch in Google Scholar
  13. Ebtehaj, I., Bonakdari, H., 2014. Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe. Water Sci. Technol., 70, 1695-1701.
    Open DOISearch in Google Scholar
  14. Ebtehaj, I., Bonakdari, H., 2016. Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms. KSCE J. Civil Eng., 20, 581-589.
    Open DOISearch in Google Scholar
  15. Ebtehaj, I., Bonakdari, H., Sharifi, A., 2014. Design criteria for sediment transport in sewers based on self-cleansing concept. J. Zhejiang-Univ. Sci-A., 15, 914-924.
    Open DOISearch in Google Scholar
  16. Ebtehaj, I., Bonakdari, H., Khoshbin, F., Azimi, H., 2015. Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices. Flow Meas. Instrum., 41, 67-74.
    Open DOISearch in Google Scholar
  17. El-Zaemey, A.K.S., 1991. Sediment transport over deposited beds in sewers. PhD Thesis, Newcastle University, Newcastle Upon Tyne, UK.
    Search in Google Scholar
  18. Gocić, M., Motamedi, S., Shamshirband, S., Petković, D., Ch, S., Hashim, R., Arif, M., 2015. Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agr., 113, 164-173.
    Open DOISearch in Google Scholar
  19. Haddadchi, A., Movahedi, N., Vahidi, E., Omid, M.H., Dehghani, A.A., 2013. Evaluation of suspended load transport rate using transport formulas and artificial neural network models (Case study: Chelchay Catchment). J. Hydrodynamics, Ser. B., 25, 459-470.
    Open DOISearch in Google Scholar
  20. Hagan, M.T., Demuth, H.B., Mark, H., Beale, M.H., 1996. Neural Network Design. PWS Publishing Company (Open Library), Boston, USA, 1012 p.
    Search in Google Scholar
  21. Haykin, S., 1994. Neural Networks, a Comprehensive Foundation. Practice Hall, New Jersey, USA, 823 p.
    Search in Google Scholar
  22. Ho, W.H., Tsai, J.T., Lin, B.T., Chou, J.H., 2009. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchigenetic learning algorithm. Expert Syst., Appl., 36, 3216-3222.
    Open DOISearch in Google Scholar
  23. Kavousi-Fard, A., Kavousi-Fard, F., 2013. A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA. J. Exp. Theor. Artif. Intel., 25, 559-574.
    Open DOISearch in Google Scholar
  24. Kisi, O., 2008. The potential of different ANN techniques in evapotranspiration modelling. Hydrol. Process., 22, 2449-2460.
    Open DOISearch in Google Scholar
  25. Kizilöz, B., Çevik, E., Aydoğan, B., 2015. Estimation of scour around submarine pipelines with Artificial Neural Network. Appl. Ocean Res., 51, 241-251.
    Open DOISearch in Google Scholar
  26. Najafzadeh, M., Lim, S.Y., 2014. Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci. Inform., 8, 187-196.
    Open DOISearch in Google Scholar
  27. Najafzadeh, M., Barani, G.A., 2011. Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci. Iran., 18, 1207-1213.
    Open DOISearch in Google Scholar
  28. Novak, P., Nalluri, C., 1984. Incipient motion of sediment particles over fixed beds. J. Hydraul. Res., 22, 181-197.
    Open DOISearch in Google Scholar
  29. Oliver, N., 2001. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer-Verlag, Berlin Heidelberg, Berlin, Germany, 785 p.
    Search in Google Scholar
  30. Safari, M.J.S., Mohammadi, M., Manafpour, M., 2011. Incipient motion and deposition of sediment in rigid boundary channels. In: Proc. 15th Int. Conf. Transport & Sedimentation of Solid Particles. Wroclaw, Poland.
    Search in Google Scholar
  31. Salem, A.M., 2013. The effects of the sediment bed thickness on the incipient motion of particles in a rigid rectangular channel. In: Proc. 17th Int. Water Technology Conf., IWTC17, Istanbul, Turkey.
    Search in Google Scholar
  32. Shvidchenko, A.B., Pender, G., 2000. Flume study of the effect of relative depth on the incipient motion of coarse uniform sediments. Water Resour. Res., 36, 619-628.
    Open DOISearch in Google Scholar
  33. Sun, S., Yan, H., Kouyi, G.L., 2014. Artificial neural network modelling in simulation of complex flow at open channel junctions based on large data sets. Environ. Model. Softw., 62, 178-187.
    Open DOISearch in Google Scholar
  34. Vongvisessomjai, N., Tingsanchali, T., Babel, M.S., 2010. Non-deposition design criteria for sewers with part-full flow. Urban Water J., 7, 61-77.
    Open DOISearch in Google Scholar
  35. Yalin, M.S., 1977. Mechanics of Sediment Transport. Pergamon Press, Oxford, UK, 360 p.
    Search in Google Scholar
DOI: https://doi.org/10.1515/johh-2016-0031 | Journal eISSN: 1338-4333 (formerly 0042-790X) | Journal ISSN: 0042-790X
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Language: English
Page range: 252 - 260
Submitted on: Oct 16, 2015
Accepted on: May 10, 2016
Published on: Jul 8, 2016
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Keywords:
Decision tree,
Incipient motion,
Multilayer perceptron (MLP),
Froude number
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2016 Isa Ebtehaj, Hossein Bonakdari, Amir Hossein Zaji, Charles Hin Joo Bong, Aminuddin Ab Ghani, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 64 (2016): Issue 3 (September 2016)
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