Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks
This study investigates the performance of artificial neural networks in predicting the incipient sediment motion in sewers. Two neural network algorithms, i.e. feed forward neural network (FFNN) and radial basis function (RBF), were employed to estimate the critical velocity over varying sediment t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Taylor & Francis
2018
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/20410/ https://doi.org/10.1080/1573062X.2018.1455880 |
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| Summary: | This study investigates the performance of artificial neural networks in predicting the incipient sediment motion in sewers. Two neural network algorithms, i.e. feed forward neural network (FFNN) and radial basis function (RBF), were employed to estimate the critical velocity over varying sediment thickness, median grain size and water depth. Empirical data from five studies were fed into the models and the performance of each model was scrutinized based on three performance criteria. Prediction from FFNN was found to give higher accuracy than values obtained from RBF. Analysis was also extended to observe the correlation between the predicted critical velocity (Formula presented.) with calculated critical velocity (Formula presented.) using five empirical equations developed using non-linear regression analysis. Prediction by FFNN proved to have the highest accuracy compared to the RBF and the values obtained through empirical equations described in this study. |
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