A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer

• The densimetric Froude number (Fr) is studied.
• The performance the wavelet-support vector machine (SVM-Wavelet) is investigated.
• Six different models were presented in order to predict.
• The SVM and SVM-Wavelet could predict the Fr with high accuracy.
• The SVM-Wavelet was found to be better than SVM and the existing equations.

Technical design of sewer systems requires highly accurate prediction of sediment transport. In this study, the capability of the combined support vector machine-wavelet transform (SVM-Wavelet) model for the prediction of the densimetric Froude number (Fr) was compared to the single SVM and different existing sediment transport equations at the limit of deposition. The performance evaluation was performed using the R-square (R2), three relative indexes (MRE, MARE, MSRE) and three absolute indexes (ME, MAE, RMSE). The factors affecting the Fr were initially determined. After categorizing them into different dimensionless groups, six different models were found to predict the Fr. Comparisons between the obtained results showed that both the SVM and SVM-Wavelet can predict the Fr with high accuracy. However, it was found that the SVM-Wavelet (R2=0.995, MRE=0.002, MARE=0.021, MSRE=0.001, ME=0.007, MAE=0.086 and RMSE=0.114) offers higher performance than the SVM and the existing equations.