A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River- A Case Study in Malaysia
Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.
[1] D. F. Lekkas, C. Onof, M. J. Lee, and E. A. Baltas, "Application of
Artificial Neural Networks for Flood Forecasting" Global Nest: The Int.
J., vol. 6, pp. 205-211, 2004.
[2] A. H. Halff, M. H. Halff, and Azmoodeh, "Predicting runoff from
rainfall using neural networks," Proceedings of the Engineering and
Hydrology, ASCE. New York., pp. 760-765, 1993.
[3] N. Karunithi, W.J. Grenney, D. Whitley, and K. Bovee, "Neural
Networks for river flow prediction," Journal of Computing in Civil
Engineering, ASCE, vol. 8, pp. 201-220, 1994.
[4] J. Smith, and R.N. Eli, "Neural network models of rainfall runoff
process," Journal of Water Resources Planning and Management, ASCE,
vol. 121, pp. 499-580, 1995.
[5] H. K. Cigizoglu and M. Alp, "Rainfall-Runoff modelling using three
Neural Network Methods," In L. Rutkowski, J. Siekmann, R.
Tadeusiewicz, and L. A. Zadeh, (Eds). Artificial Intelligence and Soft
Computing - ICAISC 2004. ed: Springer Berlin / Heidelberg, vol. 3070
pp. 166-171, 2004.
[6] H. K. Cigizoglu, "Estimation and forecasting of daily suspended
sediment data by multi-layer perceptrons," Advances in Water
Resources, vol. 27, pp. 185-195, 2004.
[7] H. K. Cigizoglu and M. Alp, "Generalized regression neural network in
modelling river sediment yield," Adv. Eng. Softw., vol. 37, pp. 63-68,
2006.
[8] V. Jothiprakash and V. Garg, "Reservoir sedimentation estimation using
Artificial Neural Network," Journal of Hydrologic Engineering, vol. 14,
pp. 1035-1040, 2009.
[9] H. M. Nagy, K. Watanabe, and M. Hirano, "Prediction of sediment load
concentration in rivers using Artificial Neural Network Model," Journal
of Hydraulic Engineering, vol. 128, pp. 588-595, 2002.
[10] M. Alp and H. K. Cigizoglu, "Suspended sediment load simulation by
two artificial neural network methods using hydrometeorological data,"
Environmental Modelling & Software, vol. 22, pp. 2-13, 2007.
[11] D. Broomhead and D. Lowe, "Multivariable functional interpolation and
adaptive networks," Complex Systems, vol. 2, pp. 321-355, 1988.
[12] A. W. Jayawardena and D. A. K. Fernando, "Use of Radial Basis
Function Type Artificial Neural Networks for runoff simulation,"
Computer-Aided Civil and Infrastructure Engineering, vol. 13, pp. 91-
99, 1998.
[13] S. Suhaimi, Rosmina A. Bustami, "Rainfall Runoff Modeling using
Radial Basis Function Neural Network for Sungai Tinjar Catchment,
Miri, Sarawak," UNIMAS E-Journal of Civil Engineering, vol. 1, 2009.
[14] L. Ma, K. Xin, and S. Liu, "Using Radial Basis Function Neural
Network to calibrate Water Quality Model," World Academy of Science,
Engineering and Technology, vol. 38, pp. 385-393, 2008.
[15] M. R. Mustafa, R. B. Rezaur, S. Saiedi, and M. H. Isa, "River Suspended
Sediment Prediction using various MLP Training Algorithms- A Case
Study in Malaysia," Submitted to Water Resources Managment, 2010.
[16] D. R. Hush and B. G. Horne, "Progress in supervised neural networks:
What's new since Lippmann?," IEEE Signal Processing magazine, pp. 8-
39, 1993.
[17] H. R. Maier and G. C. Dandy, "The use of artificial neural networks for
the prediction of water quality parameters," Water Resources Research.,
vol. 32, pp. 1013-1022, 1996.
[18] R. Rogers, " Neural Networks: A Systematic Introduction," Springer
Verlag, Berlin., pp. 151-184, 1996.
[1] D. F. Lekkas, C. Onof, M. J. Lee, and E. A. Baltas, "Application of
Artificial Neural Networks for Flood Forecasting" Global Nest: The Int.
J., vol. 6, pp. 205-211, 2004.
[2] A. H. Halff, M. H. Halff, and Azmoodeh, "Predicting runoff from
rainfall using neural networks," Proceedings of the Engineering and
Hydrology, ASCE. New York., pp. 760-765, 1993.
[3] N. Karunithi, W.J. Grenney, D. Whitley, and K. Bovee, "Neural
Networks for river flow prediction," Journal of Computing in Civil
Engineering, ASCE, vol. 8, pp. 201-220, 1994.
[4] J. Smith, and R.N. Eli, "Neural network models of rainfall runoff
process," Journal of Water Resources Planning and Management, ASCE,
vol. 121, pp. 499-580, 1995.
[5] H. K. Cigizoglu and M. Alp, "Rainfall-Runoff modelling using three
Neural Network Methods," In L. Rutkowski, J. Siekmann, R.
Tadeusiewicz, and L. A. Zadeh, (Eds). Artificial Intelligence and Soft
Computing - ICAISC 2004. ed: Springer Berlin / Heidelberg, vol. 3070
pp. 166-171, 2004.
[6] H. K. Cigizoglu, "Estimation and forecasting of daily suspended
sediment data by multi-layer perceptrons," Advances in Water
Resources, vol. 27, pp. 185-195, 2004.
[7] H. K. Cigizoglu and M. Alp, "Generalized regression neural network in
modelling river sediment yield," Adv. Eng. Softw., vol. 37, pp. 63-68,
2006.
[8] V. Jothiprakash and V. Garg, "Reservoir sedimentation estimation using
Artificial Neural Network," Journal of Hydrologic Engineering, vol. 14,
pp. 1035-1040, 2009.
[9] H. M. Nagy, K. Watanabe, and M. Hirano, "Prediction of sediment load
concentration in rivers using Artificial Neural Network Model," Journal
of Hydraulic Engineering, vol. 128, pp. 588-595, 2002.
[10] M. Alp and H. K. Cigizoglu, "Suspended sediment load simulation by
two artificial neural network methods using hydrometeorological data,"
Environmental Modelling & Software, vol. 22, pp. 2-13, 2007.
[11] D. Broomhead and D. Lowe, "Multivariable functional interpolation and
adaptive networks," Complex Systems, vol. 2, pp. 321-355, 1988.
[12] A. W. Jayawardena and D. A. K. Fernando, "Use of Radial Basis
Function Type Artificial Neural Networks for runoff simulation,"
Computer-Aided Civil and Infrastructure Engineering, vol. 13, pp. 91-
99, 1998.
[13] S. Suhaimi, Rosmina A. Bustami, "Rainfall Runoff Modeling using
Radial Basis Function Neural Network for Sungai Tinjar Catchment,
Miri, Sarawak," UNIMAS E-Journal of Civil Engineering, vol. 1, 2009.
[14] L. Ma, K. Xin, and S. Liu, "Using Radial Basis Function Neural
Network to calibrate Water Quality Model," World Academy of Science,
Engineering and Technology, vol. 38, pp. 385-393, 2008.
[15] M. R. Mustafa, R. B. Rezaur, S. Saiedi, and M. H. Isa, "River Suspended
Sediment Prediction using various MLP Training Algorithms- A Case
Study in Malaysia," Submitted to Water Resources Managment, 2010.
[16] D. R. Hush and B. G. Horne, "Progress in supervised neural networks:
What's new since Lippmann?," IEEE Signal Processing magazine, pp. 8-
39, 1993.
[17] H. R. Maier and G. C. Dandy, "The use of artificial neural networks for
the prediction of water quality parameters," Water Resources Research.,
vol. 32, pp. 1013-1022, 1996.
[18] R. Rogers, " Neural Networks: A Systematic Introduction," Springer
Verlag, Berlin., pp. 151-184, 1996.
@article{"International Journal of Architectural, Civil and Construction Sciences:62601", author = "M.R. Mustafa and M.H. Isa and R.B. Rezaur", title = "A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River- A Case Study in Malaysia", abstract = "Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.", keywords = "ANN, discharge, modeling, prediction, suspendedsediment,", volume = "5", number = "9", pages = "394-5", }
