Modeling discharge-suspended sediment relationship using least square support vector machine

SummaryThe ability of least square support vector machine (LSSVM) is investigated in this paper for modeling discharge-suspended sediment relationship. The daily stream flow and suspended sediment concentration data from two stations on the Eel River in California were used as case studies. In the first part of the study, the LSSVM was compared with those of the artificial neural networks (ANNs) and sediment rating curve (SRC) in the prediction of upstream and downstream station sediment data, separately. Two different algorithms, Levenberg–Marquardt and Conjugate Gradient, were employed for the ANN applications. For evaluating the ability of the models, root mean square errors, mean absolute errors and determination coefficient statistics were used. Comparison results showed that the LSSVM model was able to produce better results than the ANN models. LSSVM and ANN models were found to be better than the SRC model for the upstream station. For the downstream station, however, SRC model outperformed the LSSVM and ANN models. In the second part of the study, the models were compared to each other in estimation of downstream suspended sediment data by using data from both stations. It was found that the LSSVM model performs slightly better than the ANN models and both models performed much better than the SRC model.

► Suspended sediment is modeled using least square support vector machine (LSSVM).
► LSSVM results are compared with two different neural network (NN) models.
► LSSVM model is found to be superior to the NN models.
► Downstream sediment data can be successfully estimated by LSSVM using upstream data.