A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River- A Case Study in Malaysia

Authors: M.R. Mustafa, M.H. Isa, R.B. Rezaur

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,

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081399

References:

[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.