Skip to main content
SpringerLink
Log in

River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

  • Published:
Water Resources Management Aims and scope Submit manuscript

An Erratum to this article was published on 17 April 2012

Abstract

Estimation of suspended sediment discharge in rivers has a vital role in dealing with water resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) feed forward neural network with four different training algorithms was used to predict the suspended sediment discharge of a river (Pari River at Silibin) in Peninsular Malaysia. The training algorithms are Gradient Descent (GD), Gradient Descent with Momentum (GDM), Scaled Conjugate Gradient (SCG), and Levenberg Marquardt (LM). Different statistical measures, time of convergence and number of epochs to reach the required accuracy were used to evaluate the performance of training algorithms. The analysis showed that SCG and LM performed better than GD and GDM. While the performance of the superior algorithms (i.e., SCG and LM) is similar, LM required considerably shorter time of convergence. It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. However, LM was the faster (1/7 of SCG convergence time) of the two algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Agarwal A, Rai RK, Upadhyay A (2009) Forecasting of runoff and sediment yield using Artificial Neural Networks. J Water Resour Prot 1:368–375

    Article  Google Scholar 

  • Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydro-meteorological data. Environ Modell Softw 22(1):2–13

    Article  Google Scholar 

  • Bhadra A, Bandyopadhyay A, Singh R, Raghuwanshi N (2010) Rainfall-runoff modeling: comparison of two approaches with different data requirements. Water Resour Manag 24:37–62

    Article  Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, 482

  • Caudill M (1987) Neural Networks Primer, Part I. AI Expert 2(12):46–52, December 1987

    Google Scholar 

  • Chou W-C (2010) Modelling watershed scale soil loss prediction and sediment yield estimation. Water Resour Manag 24:2075–2090

    Article  Google Scholar 

  • Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27(2):185–195

    Article  Google Scholar 

  • Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

    Article  Google Scholar 

  • Cigizoglu KH, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317(3–4):221–238

    Article  Google Scholar 

  • Darus A, Ab Ghani, A Zakaria NA, Abdullah R (2003) Evaluation of Alluvial River stability for river restoration: case study of Raia River and Pari River. Proceedings of the East-Asia Regional Seminar on River Restoration, Kuala Lumpur, 13th–15th January

  • Dawson CW, Harpham C, Wilby RL, Chen Y (2002) Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China. Hydrol Earth Syst Sci 6(4):619–626

    Article  Google Scholar 

  • Deift P, Zhou X (1993) A steepest descent method for oscillatory Riemann-Hilbert problems Asymptotics for the MKdV equation. Ann Math 137(2):295–368

    Article  Google Scholar 

  • Demirel MC, Anabela V, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana Basin, Portugal. Adv Eng Softw 40:467–473

    Article  Google Scholar 

  • DID (1988) Hydrological data: Streamflow and river suspended sediment records. Ministry of Agriculture, Malaysia

    Google Scholar 

  • Edossa D, Babel M (2011) Application of ANN-based streamflow forecasting model for agricultural water management in the Awash River Basin, Ethiopia. Water Resour Manag 25:1759–1773

    Article  Google Scholar 

  • Evsukoff A, Lima B, Ebecken N (2011) Long-term runoff modeling using rainfall forecasts with application to the Iguaçu River Basin. Water Resour Manag 25:963–985

    Article  Google Scholar 

  • Fernando DAK, Shamseldin A (2009) Investigation of internal functioning of the radial basis function neural network river flow forecasting models. J Hydrol Eng ASCE 14(3):286–292

    Article  Google Scholar 

  • Guven A, Kişi Ö (2011) Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming. Water Resour Manag 25:691–704

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  • Jayawardena AW, Fernando DAK (1998) Use of radial basis function type artificial neural network for runoff simulation. Comput Aided Civ Infrastruct Eng 3(2):91–99

    Article  Google Scholar 

  • Jothiprakash V, Garg V (2009) Reservoir sedimentation estimation using artificial neural network. J Hydrol Eng 14(9):1035–1040

    Article  Google Scholar 

  • Ju Q, Yu Z, Hao Z, Ou G, Zhao J, Liu D (2009) Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72:2873–2883

    Article  Google Scholar 

  • Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50(4):693–696

    Google Scholar 

  • Kisi O (2008) Constructing neural network sediment estimation models using a data-driven algorithm. Math Comput Simulat 79(1):94–103

    Article  Google Scholar 

  • Kisi O, Haktanir T (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40(6):438–444

    Article  Google Scholar 

  • Kisi O, Nia A, Gosheh M, Tajabadi M, Ahmadi A (2011) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 1–18

  • Krause P, Byole DP, Base F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  • Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resources variables in river systems: Current status and future directions. Environ Model Softw 25:891–909

    Article  Google Scholar 

  • May DB, Sivakumar M (2009) Prediction of urban storm water quality using artificial neural networks. Environ Model Softw 24:296–302

    Article  Google Scholar 

  • Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98(5):855–866

    Article  Google Scholar 

  • Mohamed A, Ouillon S (2007) Suspended sediment transport in a semiarid watershed, Wadi Abd, Algeria (1973–1995). J Hydrol 343(3–4):187–202

    Google Scholar 

  • Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Article  Google Scholar 

  • Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588–595

    Article  Google Scholar 

  • Panagoulia D (2006) Artificial neural networks and high and low flows in various climate regimes. Hydrol Sci J 51(4):563–587

    Article  Google Scholar 

  • Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151

    Article  Google Scholar 

  • Qin M, Richard HJ, Yuan Z, Mark WJ, Bo S (2007) The effects of sediment-laden waters on irrigated lands along the lower Yellow River in China. J Environ Manag 85(4):858–865

    Article  Google Scholar 

  • Rafik Z, Ridha B, Daniel R (2008) Levenberg-Marqurdt learning neural network for adaptive pre-distortion for time varying HPA with memory in OFDM systems. 16th European Signal Processing Conference, August 25–29, Lausanne, Switzerland

  • Rojas R (1996) Neural networks: A systematic introduction. Springer Verlag, Berlin, pp 151–184

    Google Scholar 

  • Samseldin AY (1997) Application of neural network technique to rainfall runoff modeling. J Hydrol 199:272–294

    Article  Google Scholar 

  • Schraudolph NN, Graepel T (2002) Towards stochastic conjugate gradient methods. In proceedings of the 9th International conference on neural information processing ICONIP 2002. Piscataway, NJ, USA: IEEE, 853–856

  • Sinnakaudan SK, Ab Ghani A, Ahmad MSS, Zakaria NA (2003) Flood risk mapping for Pari River incorporating sediment transport. J Environ Modell Softw 18(2):119–130

    Article  Google Scholar 

  • Sinnakaudan SK, Ab Ghani A, Ahmad MSS, Zakaria NA (2006) Multiple linear regression model for total bed material load prediction. J Hydraul Eng 132(5):521–528

    Article  Google Scholar 

  • Smith J, Eli RN (1995) Neural network models of rainfall runoff process. J Water Resour Plan Manag ASCE 121(6):499–580

    Article  Google Scholar 

  • Talebizadeh M, Morid S, Ayyoubzadeh SA, Ghasemzadeh M (2010) Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour Manag 24:1747–1761

    Article  Google Scholar 

  • Tawfik M, Ibharim A, Fahmy H (1997) Hysteresis sensitive Neural Network for modeling rating curves. J Comput Civil Eng ASCE 11(3):206–211

    Article  Google Scholar 

  • Tayfur G (2002) Artificial neural networks for sheet sediment transport/Application des réseaux de neurones artificiels pour le transport sédimentaire en nappe. Hydrol Sci J 47(6):879–892

    Article  Google Scholar 

  • Wang Y, Kim S, Principe JC (2005) Comparison of TDNN training algorithms in brain machine interfaces. International Joint Conference on Neural Networks, IJCNN2005

  • Zhu YM, Lu XX, Zhou Y (2007) Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology 84:111–125

    Article  Google Scholar 

Download references

Acknowledgment

The authors acknowledge the Department of Irrigation and Drainage (DID) Malaysia for providing data for this study. The first author would like to acknowledge the financial support provided by the Universiti Teknologi PETRONAS as PhD scholarship.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Universiti Teknologi Petronas, Bandar Seri Iskandar, 31750, Tronoh, Perak Darul Ridzuan, Malaysia

    M. R. Mustafa, S. Saiedi & M. H. Isa

  2. Golder Associates Ltd, 102, 2535-3rd Avenue S.E., Calgary, T2A 7W5, Alberta, Canada

    R. B. Rezaur

Authors
  1. M. R. Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. B. Rezaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Saiedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. H. Isa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. H. Isa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mustafa, M.R., Rezaur, R.B., Saiedi, S. et al. River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia. Water Resour Manage 26, 1879–1897 (2012). https://doi.org/10.1007/s11269-012-9992-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-012-9992-5

Keywords

Search

Navigation