Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Affiliation(s)

Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.

Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.

Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.

Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.

ABSTRACT

Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

Cite this paper

H. Memarian and S. Balasundram, "Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed,"*Journal of Water Resource and Protection*, Vol. 4 No. 10, 2012, pp. 870-876. doi: 10.4236/jwarp.2012.410102.

H. Memarian and S. Balasundram, "Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed,"

References

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[20] M. Kim and J. E. Gilley, “Artificial Neural Network Estimation of Soil Erosion and Nutrient Concentrations in Runoff from Land Application Areas,” Computers and Electronics in Agriculture, Vol. 64, No. 2, 2008, pp. 268- 275. doi.10.1016/j.compag.2008.05.021

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[27] M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris and D. M. Hummels, “On the Training of Radial Basis Function Classifiers,” Neural Network, Vol. 5, No. 4, 1992, pp. 595-603. doi.10.1016/S0893-6080(05)80038-3

[28] M. Alp and H. K. Cigizoglu, “Suspended Sediment Load Simulation by Two Artificial Neural Network Methods Using Hydrometeorological Data,” Environmental Modelling and Software, Vol. 22, No. 1, 2007, pp. 2-13. doi.10.1016/j.envsoft.2005.09.009

[29] K. L. Hsu, H. Gupta and S. Sorooshian, “Artificial Neural Network Modeling of the Rainfall Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955

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[31] H. K. Cigizoglu and O. Kisi, “Methods to Improve the Neural Network Performance in Suspended Sediment Estimation,” Journal of Hydrology, Vol. 317, No. 3-4, 2006, pp. 221-238. doi.10.1016/j.jhydrol.2005.05.019

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[1] C. Chutachindakate and T. Sumi, “Sediment Yield and Transportation Analysis: Case Study on Managawa River Basin,” Annual Journal of Hydraulic Engineering, Vol. 52, 2008, pp. 157-162.

[2] B. Zhang and R. S. Govindaraju, “Geomorphology-Based Artificial Neural Networks (GANNs) for Estimation of Direct Runoff over Watersheds,” Journal of Hydrology, Vol. 273, No. 1-4, 2003, pp. 18-34. doi.10.1016/S0022-1694(02)00313-X

[3] G. Singh and R. K. Panda, “Daily Sediment Yield Modeling with Artificial Neural Network Using 10-Fold cross Validation Method: A Small Agricultural Watershed, Kapgari, India,” International Journal of Earth Sciences and Engineering, Vol. 4, No. 6, 2011, pp. 443-450.

[4] M. R. Mustafa, M. H. Isa and R. B. Rezaur, “A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River—A Case Study in Malaysia,” World Academy of Science, Engineering and Technology, Vol. 81, 2011, pp. 372-376.

[5] H. Halff, M. H. Halff and Azmoodeh, “Predicting Runoff from Rainfall Using Neural Networks,” Proceedings of the Engineering and Hydrology, New York, 1993, pp. 760-765.

[6] N. Karunithi, W. J. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engineering, Vol. 8, 1994, pp. 201- 220.

[7] J. Smith and R. N. Eli, “Neural Network Models of Rain- fall Runoff Process,” Journal of Water Resources Planning and Management, Vol. 121, No. 6, 1995, pp. 499- 580. doi.10.1061/(ASCE)0733-9496(1995)121:6(499)

[8] D. F. Lekkas, C. Onof, M. J. Lee and E. A. Baltas, “Ap- plication of Artificial Neural Networks for Flood Forecasting,” Global Nest: The International Journal, Vol. 6, No. 3, 2004, pp. 205-211.

[9] 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, Springer, Berlin, Heidelberg, Vol. 3070, 2004, pp. 166- 171.

[10] H. K. Cigizoglu, “Estimation and Forecasting of Daily Suspended Sediment Data by Multi-Layer Perceptrons,” Advances in Water Resources, Vol. 27, No. 2, 2004, pp. 185-195. doi.10.1016/j.advwatres.2003.10.003

[11] S. S. Eslamian, S. A. Gohari, M. Biabanaki and R. Malekian, “Estimation of Monthly Pan Evaporation Using Ar- tificial Neural Networks and Support Vector Machines,” Journal of Applied Sciences, Vol. 8, No. 19, 2008, pp. 3497-3502.

[12] V. Jothiprakash and V. Garg, “Reservoir Sedimentation Estimation Using Artificial Neural Network,” Journal of Hydrologic Engineering, Vol. 14, 2009, pp. 1035-1040. doi.10.1061/(ASCE)HE.1943-5584.0000075

[13] H. Memarian, S. Feiznia and S. Zakikhani, “Estimating River Suspended Sediment Yield Using MLP Neural Network in Arid and Semi-Arid Basins, Case Study: Bar river, Neyshaboor, Iran.” Desert, Vol. 14, 2009, pp. 43- 52.

[14] M. Talebizadeh, S. Morid, S. A. Ayyoubzadeh and M. Ghasemzadeh, “Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model,” Water Re- sources Management, Vol. 24, No. 9, 2010, pp. 1747- 1761. doi. 10.1007/s11269-009-9522-2

[15] A. Singh, M. Imtiyaz, R. K. Isaacc and D. M. Denisc, “Comparison of Soil and Water Assessment Tool (SWAT) and Multilayer Perceptron (MLP) Artificial Neural Network for Predicting Sediment Yield in the Nagwa Agricultural Watershed in Jharkhand, India,” Agricultural Water Management, Vol. 104, 2012, pp. 113-120. doi.10.1016/j.agwat.2011.12.005

[16] H. Memarian, S. K. Balasundram, J. Talib, C. B. S. Teh, M. S. Alias, K. C. Abbaspour and A. Haghizadeh, “Hydrologic Analysis of a Tropical Watershed Using Kineros 2,” Environment Asia, Vol. 5, No. 1, 2012, pp. 84-93.

[17] H. Memarian, S. K. Balasundram, J. Talib, M. S. Alias and K. C. Abbaspour, “Trend Analysis of Water Dis- charge and Sediment Load during the Past Three Decades of Development in the Langat Basin, Malaysia,” Hydrological Sciences Journal, Vol. 57, No. 6, 2012, pp. 1207- 1222. doi.10.1080/02626667.2012.695073

[18] E. Rumelhart, J. L. McClelland and the PDP Research Group, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations,” MIT Press, Cambridge, 1986.

[19] J. T. Kuo, M. H. Hsieh, W. S. Lung and N. She, “Using Artificial Neural Network for Reservoir Entrophication Prediction,” Ecological Modelling, Vol. 200, No. 1-2, 2007, pp. 171-177. doi.10.1016/j.ecolmodel.2006.06.018

[20] M. Kim and J. E. Gilley, “Artificial Neural Network Estimation of Soil Erosion and Nutrient Concentrations in Runoff from Land Application Areas,” Computers and Electronics in Agriculture, Vol. 64, No. 2, 2008, pp. 268- 275. doi.10.1016/j.compag.2008.05.021

[21] J. C. Principe, W. C. Lefebvre, G. Lynn, C. Fancourt and D. Wooten, “NeuroSolutions-Documentation, the Manual and On-Line Help,” 2007.

[22] T. Rajaee, S. A. Mirbagheri, M. Zounemat-Kermani and V. Nourani, “Daily Suspended Sediment Concentration Simulation Using ANN and Neuro-Fuzzy Models,” Sci- ence of the Total Environment, Vol. 407, No. 17, 2009, pp. 4916-4927. doi.10.1016/j.scitotenv.2009.05.016

[23] P. Baldi, “Gradient Descent Learning Algorithm Overview: A General Dynamical Systems Perspective,” IEEE Transactions on Neural Networks, Vol. 6, No. 1, 1995, pp. 182-195.

[24] J. C. Principe, N. R. Euliano and W. C. Lefebvre, “Neural and Adaptive Systems: Fundamentals through Simulations,” John Wiley & Sons Inc., Hoboken, 2000.

[25] D. Graupe, “Principles of Artificial Neural Networks (2nd Edition), Advanced Series on Circuits and Systems,” Vol. 6, World Scientific Publishing, Singapore City, 2007.

[26] F. Lin and L. H. Chen, “A Non-Linear Rainfall-Runoff Model Using Radial Basis Function Network,” Journal of Hydrology, Vol. 289, No. 1-4, 2004, pp. 1-8. doi.10.1016/j.jhydrol.2003.10.015

[27] M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris and D. M. Hummels, “On the Training of Radial Basis Function Classifiers,” Neural Network, Vol. 5, No. 4, 1992, pp. 595-603. doi.10.1016/S0893-6080(05)80038-3

[28] M. Alp and H. K. Cigizoglu, “Suspended Sediment Load Simulation by Two Artificial Neural Network Methods Using Hydrometeorological Data,” Environmental Modelling and Software, Vol. 22, No. 1, 2007, pp. 2-13. doi.10.1016/j.envsoft.2005.09.009

[29] K. L. Hsu, H. Gupta and S. Sorooshian, “Artificial Neural Network Modeling of the Rainfall Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955

[30] S. Morid, A. K. Gosain and A. K. Keshari, “Solar Radia- tion Estimation Using Temperature-Based, Stochastic and Artificial Neural Networks Approaches,” Nordic Hydrology, Vol. 3, No. 4, 2002, pp. 291-304. doi:10.2166/nh.2002.017

[31] H. K. Cigizoglu and O. Kisi, “Methods to Improve the Neural Network Performance in Suspended Sediment Estimation,” Journal of Hydrology, Vol. 317, No. 3-4, 2006, pp. 221-238. doi.10.1016/j.jhydrol.2005.05.019

[32] K. C. Abbaspour, “User Manual for SWAT-CUP4, SWAT Calibration and Uncertainty Analysis Programs,” Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 2011.

[33] C. Jones, M. Sultan, E. Yan, A. Milewski, M. Hussein, A. Al-Dousari, S. Al-Kaisy and R. Becker, “Hydrologic Impacts of Engineering Projects on the Tigris-Euphrates System and Its Marshlands,” Journal of Hydrology, Vol. 353, No. 1-2, 2008, pp. 59-75. doi.10.1016/j.jhydrol.2008.01.029.