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

Isa Ebtehaj 1 , 2 , Hossein Bonakdari 1 , 2 , Amir Hossein Zaji 1 , 2 , Charles Hin Joo Bong 3 , 4  and Aminuddin Ab Ghani 4
  • 1 Department of Civil Engineering, Razi University, 67149-67346 Baghe Abrisham, Kermanshah, Iran
  • 2 Water and Wastewater Research Center, Razi University, 67149-67346 Baghe Abrisham, Kermanshah, Iran
  • 3 Department of Civil Engineering, Faculty of Engineering, University Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
  • 4 River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia (USM), 14300 Nibong Tebal, Pulau Pinang, Malaysia

Abstract

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

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  • 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.

  • 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.

  • 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.

  • Ackers, P., White, W.R., 1973. Sediment transport: new approach and analysis. J. Hydraul. Div., 99, 2041-2060.

  • Ahmad, Z., Azamathulla, H.Md., Zakaria, N.A., 2011. ANFISbased approach for the estimation of transverse mixing coefficient. Water Sci. Technol., 63, 1005-1010.

  • 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.

  • 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.

  • 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.

  • 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.

  • Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1993. Classification and Regression Trees. Wadsworth, Inc. California, USA, 368 p.

  • Christopher, M., 1995. Neural networks for pattern recognition. Oxford University Press, Oxford, UK, 482 p.

  • Ebtehaj, I., Bonakdari, H., 2013. Evaluation of sediment transport in sewer using artificial neural network. Eng. Appl. Comput. Fluid Mech., 7, 382-392.

  • 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.

  • 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.

  • 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.

  • 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.

  • El-Zaemey, A.K.S., 1991. Sediment transport over deposited beds in sewers. PhD Thesis, Newcastle University, Newcastle Upon Tyne, UK.

  • 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.

  • 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.

  • Hagan, M.T., Demuth, H.B., Mark, H., Beale, M.H., 1996. Neural Network Design. PWS Publishing Company (Open Library), Boston, USA, 1012 p.

  • Haykin, S., 1994. Neural Networks, a Comprehensive Foundation. Practice Hall, New Jersey, USA, 823 p.

  • 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.

  • 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.

  • Kisi, O., 2008. The potential of different ANN techniques in evapotranspiration modelling. Hydrol. Process., 22, 2449-2460.

  • Kizilöz, B., Çevik, E., Aydoğan, B., 2015. Estimation of scour around submarine pipelines with Artificial Neural Network. Appl. Ocean Res., 51, 241-251.

  • 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.

  • 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.

  • Novak, P., Nalluri, C., 1984. Incipient motion of sediment particles over fixed beds. J. Hydraul. Res., 22, 181-197.

  • Oliver, N., 2001. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer-Verlag, Berlin Heidelberg, Berlin, Germany, 785 p.

  • 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.

  • 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.

  • 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.

  • 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.

  • Vongvisessomjai, N., Tingsanchali, T., Babel, M.S., 2010. Non-deposition design criteria for sewers with part-full flow. Urban Water J., 7, 61-77.

  • Yalin, M.S., 1977. Mechanics of Sediment Transport. Pergamon Press, Oxford, UK, 360 p.

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