Suspended sediment load estimate using support vector machines in Kaoping river basin

Strong correlation exists between river discharge and suspended sediment load. The relationship was used to estimate suspended sediment load by using linear regression model, power regression model, artificial neural network and support vector machine in this study. Records of river discharges and suspended sediment loads in Kaoping river basin were investigated as case study. Eighty-five percent of the records were used as training data set to develop those four models. The other fifteen percent records were used as verification data set. The performance of the four models was evaluated by root mean square errors (RMSE). The RMSEs show: support vector machine <; artificial neural network <; power regression model <; linear regression model. The result shows that SVM outperforms the ANN and other two regression models. Therefore, SVM approach was proposed to estimate the river suspended sediment load.