Abstract
ABSTRACTA modified multi-layer perceptron (MLP) model based on decision trees (DT-MLP) is presented to predict velocity and water free-surface profiles in a 90° open-channel bend. The ability of the new hybrid model to predict the velocity and flow depth in a 90° sharp bend is investigated and compared with the abilities of MLP and multiple-linear regression (MLR) models. The MLP and DT-MLP networks are trained and tested using 520 and 506 experimental data measured for velocity and flow depth, respectively, at five different discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 l/s. The MLP and DT-MLP comparison results against MLR reveal that the two artificial neural networks (ANNs) are 84% and 16% more accurate than the MLR model in predicting the velocity and flow depth variables, respectively. According to the results, the root mean square error (RMSE) value of the DT-MLP model decreases by 9% and 7.5% in predicting velocity and flow depth, respectively, compared with the MLP model. It was found that the hybrid decision-tree-based method can significantly improve MLP neural network performance in forecasting velocity and free-surface profiles in a 90° open-channel bend.
Highlights
Most rivers and open channels have curved paths
An attempt was made in the present research to evaluate the performance of an multi-layer perceptron (MLP) model in predicting velocity and water surface variables in a 90° bend before and after the model was combined with a decision tree (MLP vs. decision-tree-based multi-layer perceptron (DT-MLP))
A total of 520 and 506 depth-averaged velocity and water surface experimental samples were used for training and testing the networks at five different discharge rates
Summary
Most rivers and open channels have curved paths. it is essential to understand the hydraulic behavior of flow in bends. The flow pattern in a 270° sharp bend as well as the velocity components and water surface level within the channel were numerically modeled and examined by DeMarchis and Napoli (2006). Baghalian, Bonakdari, Nazari, and Fazli (2012) studied and compared the performance of MLP with an analytical solution and a numerical model to investigate the flow patterns in curved channels. Vast experimental tests were undertaken by the authors in a 90° sharp bend to train and test the networks (Akhtari, Abrishami, & Sharifi, 2009; Bahrami, Ghaneeizad, & Akhtari, 2009) The inputs to both models are the points coordinates (X and Y) and different flow discharge rates (Q), while the outputs are velocity and flow depth. The MLP and DT-MLP model performance is compared with the experimental results from the prediction of flow variables at different discharge rates
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