عنوان مقاله [English]
Estimation of the suspended sediment load plays a very important role in water projects such as dam and surface water storage design, pollution and sediment transport in rivers and lakes, as well as design and maintenance the channels. The accuracy of conventional methods of sediment estimation, usually low and there are a big difference between their results. Therefore, in this study after preparing sediment and flow discharge data of hydrometric stations; Jow Kanak, Behbahan, Shadegan, Moshrageh and Cham Nezam, the M5 algorithm, has been used to determine the suspended sediment load. M5 tree algorithm is one of the newest data-driven models which divides the problem space into multiple branches and proposes equations for each branches. Flow discharge of each day and the day before have been used as an input variables and sediment discharge has been considered as an output variable to build and validate the tree model. Statistical analysis on tree model showed better compliance of tree model results with observed data in compare with sediment rating curve method. RMSE for training and validation phases at studied stations were 0.59 and 0.74 for M5 and rating curve methods, respectively. The results of this study showed that suspended sediment load could be accurately predicted using M5 tree algorithm. The results of this model, in addition to have an understandable and simple structure, could be used in practical issues as well.