Wan Mohtar, Wan Hanna Melini and Afan, Haitham and El-Shafie, Ahmed and Bong, Charles Hin Joo and Ab. Ghani, Aminuddin (2018) Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks. Urban Water Journal, 15 (4). pp. 296-302. ISSN 1573-062X, DOI https://doi.org/10.1080/1573062X.2018.1455880.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Kebangsaan Malaysia [grant number through DIP-2015-006], Ministry of Science, Technology and Innovation with the [grant number 06-01-02-SF1077], Universiti Malaysia Sarawak: SGS [grant number F02/SpGS/1542/2017] |
| Uncontrolled Keywords: | Incipient sediment motion; sewers; sediment bed thickness; artificial neural networks |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Feb 2019 02:35 |
| Last Modified: | 21 Feb 2019 02:35 |
| URI: | http://eprints.um.edu.my/id/eprint/20410 |
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