New radial basis function network method based on decision trees to predict flow variables in a curved channel

Open channel bends have fascinated engineers and scientists for decades while providing water for domestic, irrigation and industrial consumption. The presence of curvature in a channel impacts the flow pattern, velocity and water surface profile. Simulating flow variables such as velocity and water surface depth is one of the most important matters in the design and application of open channel bends. This study investigates a new neural network method using the radial basis function (RBF) based on decision trees (DT-RBF) to predict velocity and free-surface water profiles in a 90 degrees open channel bend. In this study, 506 flow depth and 520 depth-averaged velocity field data obtained at 5 different discharges (5, 7.8, 13.6, 19.1 and 25.3l/s) in a 90 degrees sharp bend were used for training and testing purposes. The obtained results showed that the proposed DT-RBF models were more accurate than RBF models in estimating flow depth and depth-averaged velocity in the bend. The RBF root-mean-square error (RMSE), mean absolute error (MAE) and relative error () were reduced by 20, 24 and 23.5%, respectively, when using the hybrid DT-RBF model to estimate the depth-averaged velocity. For water surface prediction, the RMSE, MAE and decreased by 33, 27.5 and 37%, respectively, when using the proposed DT-RBF hybrid model. For the longitudinal profiles of water surface profile prediction at the outer edge, MAE (0.018) improved to MAE (0.0084) with DT-RBF. It was found that the hybrid decision tree-based method significantly improved RBF neural network performance in forecasting the velocity and free-surface water profiles in a 90 degrees open channel sharp bend.