Suspended sediment modelling by SVM and wavelet - Maedeh Sadeghpour Haji; Seyed A. Mirbagheri; Amir H. Javid; Mostafa Khezri; Ghasem D. Najafpour (Građevinar, 2014)
Present-day advances in artificial intelligence, as a forecaster for hydrological events, have led to numerous changes in forecasting. The wavelet support vector machine (WSWM) model is achieved by conjunction of the wavelet analysis and the support vector machine (SVM). The suspended sediment (SS) and daily stream flow (Q) data from the Iowa River in the USA were used for training and testing. The WSVM could logically be used for approximation of the suspended sediment load.