Application of Hybrid Wavelet-SVM and ANN in the Prediction of Suspended Sediment Load of Aji-chay

Document Type : Research Paper


Department of Water and Hydraulic Structures, Faculty of Civil Engineering, Tabriz University


Simulation and evaluation of sediment and extending the relation among streamflow and sediment is an important and applicable issue in the water resource management which is useful in the organizing reservoirs, rivers and avoiding additional costs. In this paper, the efficiency of Wavelet-based Support Vector Machine (WSVM) model was examined for prediction of monthly Suspended Sediment Load (SSL) of the Aji-Chay River. Then for this purpose, at first step, SSL was predicted via ad hoc SVM and Artificial Neural Network (ANN) models. Therefore in the hybrid models, streamflow and SSL time series were decomposed into sub-signals via wavelet transform, and these decomposed subseries were imposed into SVM and ANN to simulate discharge-SSL relationship. The results showed that SVM yield to better outcomes with Determination Coefficient (DC)= 0.65 than ad hoc ANN with DC=0.61. On the other hand, WSVM showed better consequences than wavelet-based ANN (WANN) model in monthly SSL prediction, and wavelet data pre-processing could lead to catch more accurate results, e.g., DCLSSVM=0.65 was increased to the DCWLSSVM=0.82.


Main Subjects

[1]-Hart, R.J., Anctil, F., Coulibaly, P., Dawson, C.W., Mount, N.J., See, L.M., Shamseldin, A.Y., Solomatine, D.P., Toth, E. and Wilby, R.L. (2012), “Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting,” Progress in Physical Geography, 36, pp 480-513.
[2]-Jain, A.K., Murty, M.N. and Flynn, P.J. (1999), “Data clustering: a review,” ACM Computing Surveys, 31(3), pp 264-323.
[3]-Nourani, V., Kalantari, O., Baghanam, A.H. 2012. Two semi-distributed ANN-based models for estimation of suspended sediment load. Journal of Hydrologic Engineering, 17(12): 1368–1380.
[4]-Vapnik, V. and Cortes, C. (1995), “Support Vector Networks. Machine Learning, 20,” pp 1-25.
[5]-Cimen, M. (2008), “Estimation of daily suspended sediments using support vector machines,” Hydrological Sciences Journal, 53, pp 656–666.
[6]-Jie, L.C., Yu, S.T. (2011), “Suspended sediment load estimate using support vector machines in Kaoping River basin,” International Conference on Consumer Electronics, Communications and Networks (IEEE), XianNing, China, 16-18 April.
[7]- Kakaei, E., Moghddamnia, A. and Ahmadi, A. (2013), “Daily suspended sediment load prediction using artificial neural networks and support vector machines,” Journal of Hydrology, 478, pp 50-62.
[8]- Nason, G.P. and Von Sachs, R., (1999), “Wavelets in time series analysis,” Philosophical Transactions of the Royal Society, 357, pp 2511-2526.
[9]-Aussem, A. and Murtagh, F. (1997), “Combining neural network forecasts on wavelet-transformed time series,” Connection Science, 9, pp 113-121.
[10]-Nourani, V., Baghanam, A.H., Adamowski, J. and Kisi, O, (2014), “Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review,” Journal of Hydrology, 514, pp 358-377.
[11]- Nourani, Nourani, V., Baghanam, A.H., Yahyavi Rahimi, A. and Nejad, F.H., (2014), “Evaluation of wavelet-based de-noising approach in hydrological models linked to artificial neural networks,” Computational Intelligence Techniques in Earth and Environmental Sciences. Springer Dordrecht Heidelberg NewYork London, pp 209-241.
[12]-Kisi, O. and Cimen, M. (2011), “A wavelet-support vector machine conjunction model for monthly streamflow forecasting,” Journal of Hydrology, 399, pp 132-140.
[13]- Kalteh, A.M. (2013), “Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform,” Computers and Geosciences, 54, pp 1-8.
[14]- Nourani, V. and Andalib, G. (2015), “Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches,” Journal of Mountain Science, 12:85-100.
[15]-Nourani, V. and Andalib,  G. (2015), “Wavelet based artificial intelligence approaches for prediction of hydrological time Series,” In: Chalup, S.K., Blair A.D., Randall, M., (Eds.), Artificial life and computational intelligence. Springer International Publishing. Switzerland, 8955, 422-435.