Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks

Wan Hanna Melini Wan Mohtar, Haitham Afan, Ahmed El-Shafie, Charles Hin Joo Bong, Aminuddin Ab. Ghani

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalUrban Water Journal
DOIs
Publication statusAccepted/In press - 18 Apr 2018

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neural network
artificial neural network
prediction
sediment
sediment thickness
regression analysis
water depth
performance
grain size
agricultural product
water
analysis

Keywords

  • artificial neural networks
  • Incipient sediment motion
  • sediment bed thickness
  • sewers

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

Cite this

Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks. / Wan Mohtar, Wan Hanna Melini; Afan, Haitham; El-Shafie, Ahmed; Bong, Charles Hin Joo; Ab. Ghani, Aminuddin.

In: Urban Water Journal, 18.04.2018, p. 1-7.

Research output: Contribution to journalArticle

Wan Mohtar, Wan Hanna Melini ; Afan, Haitham ; El-Shafie, Ahmed ; Bong, Charles Hin Joo ; Ab. Ghani, Aminuddin. / Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks. In: Urban Water Journal. 2018 ; pp. 1-7.
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