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A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers\udand drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment\udis estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations\uddo not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this\udstudy along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate\udincipient motion. According to a comparison with the observed experimental outcome, the proposed method performs\udwell (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = –0.036). The performance of MLP and MLP-DT is compared\udwith that of existing regression-based equations, and significantly higher performance over existing models is observed.\udFinally, an explicit expression for practical engineering is also provided

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels