Abstract:
Appropriate and acceptable prediction of bed load being carried by streams is vitally important for water resources quantity and quality studies. Although measuring the rate of bed load in situ is the most consistent method, it is very expensive and cannot be conducted for as many streams as the measurement of suspended sediment load. Therefore, in this study the role of suspended load on bedload prediction was examined by using sensitivity analysis. On the other hand, conventional sediment rating curves and equations can not predict sediment load accurately so recently the usage of machine learning algorithms increase rapidly. Accordingly, soft computational methods are used in the study. These are; artificial neural network (ANN), support vector machine (SVM) models and a decision tree (CHAID) model that is not used before in sediment studies. Some particular parameters are frequently used in these soft computational methods to form input sets. Hence, well known and commonly used three input sets and a new generated set are used as inputs to predict bedload and then the suspended load variable is added in these input sets. The performances of models with respect to input sets are compared to each other. To generate the results and to push the limits of models a very skewed and heterogeneous data is collected from distributed locations. The results indicate that the performance of ANN and CHAID tree models are good when compared to SVM models. The usage of a suspended load as an additional input for the models boosts the model performances and the suspended load has significant contributions to all models.
