Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

DOI: 10.4236/jwarp.2013.54039   PDF   HTML     3,980 Downloads   6,023 Views   Citations


Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.

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S. Heng and T. Suetsugi, "Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction," Journal of Water Resource and Protection, Vol. 5 No. 4, 2013, pp. 395-404. doi: 10.4236/jwarp.2013.54039.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] G. L. Morris and J. Fan, “Reservoir Sedimentation Handbook: Design and Management of Dams, Reservoirs, and Watershed for Sustainable Use,” McGraw-Hill, New York, 1998.
[2] USBR (United States Bureau of Reclamation), “Erosion and Sedimentation Manual,” USBR, Colorado, 2006.
[3] A. M. Melesse, S. Ahmad, M. E. McClain, X. Wang and Y. H. Lim, “Suspended Sediment Load Prediction of River Systems: An Artificial Neural Network Approach,” Agricultural Water Management, Vol. 98, No. 5, 2011, pp. 855-866. doi:10.1016/j.agwat.2010.12.012
[4] A. Singh, M. Imtiyaz, R. K. Isaac and D. M. Denis, “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, 2011, pp. 113-120. doi:10.1016/j.agwat.2011.12.005
[5] O. Kisi and J. Shiri, “River Suspended Sediment Estimation by Climatic Variables Implication: Comparative Study among Soft Computing Techniques,” Computers & Geosciences, Vol. 43, 2012, pp. 73-82. doi:10.1016/j.cageo.2012.02.007
[6] D. E. Walling and D. Fang, “Recent Trends in the Suspended Sediment Loads of the World’s Rivers,” Global and Planetary Change, Vol. 39, No. 1-2, 2003, pp. 111-126. doi:10.1016/S0921-8181(03)00020-1
[7] O. M. Rezapour, L. T. Shui and D. B. Ahmad, “Review of Artificial Neural Network Model for Suspended Sediment Estimation,” Australian Journal of Basic and Applied Sciences, Vol. 4, No. 8, 2010, pp. 3347-3353.
[8] H. R. Maier and G. C. Dandy, “Neural Networks for the Prediction and Forecasting of Water Resources Variables: A Review of Modelling Issues and Applications,” Environmental Modelling & Software, Vol. 15, No. 1, 1999, pp. 101-124. doi:10.1016/S1364-8152(99)00007-9
[9] 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.
[10] 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.
[11] H. K. Cigizoglu, “Suspended Sediment Estimation for Rivers Using Artificial Neural Networks and Sediment Rating Curves,” Turkish Journal of Engineering and Environmental Sciences, Vol. 26, No. 1, 2002, pp. 27-36.
[12] G. Tayfur, “Artificial Neural Networks for Sheet Sediment Transport,” Hydrological Sciences Journal, Vol. 47, No. 6, 2002, pp. 879-892. doi:10.1080/02626660209492997
[13] O. Kisi, “Development of Streamflow-Suspended Sediments Rating Curve Using a Range Dependent Neural Network,” International Journal of Science and Technology, Vol. 2, No. 1, 2007, pp. 49-61.
[14] C. Sivapragasam, S.-Y. Liong and M. F. K. Pasha, “Rainfall and Runoff Forecasting with SSA-SVM Approach,” Journal of Hydroinformatics, Vol. 3, No. 3, 2001, pp. 141-152.
[15] N. Golyandina, V. Nekrutkin and A. A. Zhigljavsky, “Analysis of Time Series Structure: SSA and Related Techniques,” Chapman and Hall/CRC, Boca Raton, 2001. doi:10.1201/9781420035841
[16] GistaT Group, “Time Series Analysis and Forecasting,” 2010.
[17] R. T. Hanson, M. W. Newhouse and M. D. Dettinger, “A Methodology to Assess Relations between Climatic Variability and Variations in Hydrologic Time Series in the Southwestern United States,” Journal of Hydrology, Vol. 287, No. 1-4, 2004, pp. 252-269. doi:10.1016/j.jhydrol.2003.10.006
[18] C. A. F. Marques, J. A. Ferreira, A. Rocha, J. M. Castanheira, P. Melo-Goncalves, N. Vaz and J. M. Dias, “Singular Spectrum Analysis and Forecasting of Hydrological Time Series,” Physics and Chemistry of the Earth, Vol. 31, No. 18, 2006, pp. 1172-1179. doi:10.1016/j.pce.2006.02.061
[19] H. J. Fuchs, “Data Availability for Studies on Effects of Land-Cover Changes on Water Yield, Sediment and Nutrient Load at Catchments of the Lower Mekong Basin,” MRC-GTZ Cooperation Programme, G?ttingen, 2004.
[20] H. Memarian and S. K. 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
[21] S. Heng and T. Suetsugi, “Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia,” Journal of Water Resource and Protection, Vol. 5, No. 2, 2013, pp. 111-123. doi:10.4236/jwarp.2013.52013
[22] Y. Tramblay, A. St-Hilaire and T. B. M. J. Ouarda, “Frequency Analysis of Maximum Annual Suspended Sediment Concentrations in North America,” Hydrological Sciences Journal, Vol. 53, No. 1, 2008, pp. 236-252. doi:10.1623/hysj.53.1.236
[23] P. Gao and M. Josefson, “Event-Based Suspended Sediment Dynamics in a Central New York Watershed,” Geomorphology, Vol. 139-140, 2011, pp. 425-437. doi:10.1016/j.geomorph.2011.11.007
[24] S. Heng and T. Suetsugi, “Estimating Quantiles of Annual Maximum Suspended Sediment Load in the Tributaries of the Lower Mekong River,” Journal of Water and Climate Change, Vol. 4, No. 1, 2013, pp. 63-76. doi:10.2166/wcc.2013.023
[25] O. Kisi, I. Yuksel and E. Dogan, “Modelling Daily Suspended Sediment of Rivers in Turkey Using Several Data-Driven Techniques,” Hydrological Sciences Journal, Vol. 53, No. 6, 2008, pp. 1270-1285. doi:10.1623/hysj.53.6.1270
[26] O. Kisi, “Multi-Layer Perceptrons with Levenberg-Marquardt Training Algorithm for Suspended Sediment Concentration Prediction and Estimation,” Hydrological Sciences Journal, Vol. 49, No. 6, 2004, pp. 1025-1040. doi:10.1623/hysj.49.6.1025.55720
[27] J. E. Nash and J. V. Sutcliffe, “River Flow Forecasting through Conceptual Models Part I-A Discussion of Principles,” Journal of Hydrology, Vol. 10, No. 3, 1970, pp. 282-290. doi:10.1016/0022-1694(70)90255-6
[28] D. N. Moriasi, J. G. Arnold, M. W. V. Liew, R. L. Bingner, R. D. Harmel and T. L. Veith, “Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations,” Transactions of the American Society of Agriculture and Biological Engineers, Vol. 50, No. 3, 2007, pp. 885-900.
[29] M. Shahin, H. J. L. Van Oorschot and S. J. De Lange, “Statistical Analysis in Water Resources Engineering,” A. A. Balkema, Rotterdam, 1993.
[30] T. Rajaee, “Wavelet and ANN Combination Model for Prediction of Daily Suspended Sediment Load in Rivers,” Science of the Total Environment, Vol. 409, No. 15, 2010, pp. 2917-2928. doi:10.1016/j.scitotenv.2010.11.028

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