Prediction of Froude Number of Three Phases Flow in Sewer Systems Using Extreme Learning Machines

Document Type : Technical Note

Authors

1 Assist. Prof., Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Assoc. Prof., Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Abstract

Generally, circular channels are used in urban sewage systems where the flow is a three phase flow including water, air, and sediments. Accordingly, there are many studies carried out by different researchers related to flow within sewage channels. In current study, the Froude number of three phase flow within sewer channels is predicted using Extreme Learning Machine (ELM). Using parameters affecting the Froude number, 127 various ELM models were defined. The superior model was then introduced. For instance, for the superior model as a function of volumetric sediment concentration, the ratio of the particle size to overall hydraulic radius and overall friction factor for sediment  load of 60% and 40% in train and test, respectively, the R2, MAPE and RMSE in testing mode were calculated as 0.856, 0.117, and 0.738, respectively. In addition, the results of superior model were compared with Artificial Neural Network (ANN) and support Vector Machine (SVM) models. Analyses of modeling results showed that extreme learning machine simulated the aim function with more accuracy.

Keywords

Main Subjects

References

Ab Ghani, A. 1993. Sediment transport in sewers. Ph.D. Thesis, University of Newcastle Upon Tyne, UK.
Ackers, J.C., Butler, D. & May, R.W.P. 1996. Design of sewers to control sediment problems. Construction Industry Research and Information Association, London.
Ebtehaj, I. & Bonakdari, H. 2013. Evaluation of sediment transport in sewer using artificial neural network. Engineering Applications of Computational Fluid Mechanics, 7(3), 382-392.
Ebtehaj, I., Bonakdari, H. & Zaji, A.H. 2016. A nonlinear simulation method based on a combination of multilayer perceptron and decision trees for predicting non-deposition sediment transport. Water Science and Technology: Water Supply, 16(5), 1198-1206.
Ebtehaj, I. & Bonakdari, H. 2014. Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe. Water Science and Technology, 70 (10), 1695-1701.
Huang, G-B., Zhu, Q-Y. & Siew, C-K. 2006. Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489-501.
Liang, N.Y., Huang, G.B., Rong, H.J., Saratchandran, P. & Sundararajan, N. 2006. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw, 17, 1411-1423.
Melessea, A.M., Ahmadb, S., McClaina, M.E., Wangc, X. & Limd, Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98,
855-866.
Ota, J.J. & Nalluri, C. 1999. Graded sediment transport at limit deposition in clean pipe channel. In 28th Congress of International Association Hydro-Environmental Engineering Research, Graz, Austria.
Vongvisessomjai, N., Tingsanchali, T. & Babel, M.S. 2010. Non-deposition design criteria for sewers with part-full flow. Journal of Urban Water, 7(1), 61-77.
Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 30, Issue 5 - Serial Number 123
November and December 2019
Pages 121-126

Files

Share

How to cite

Statistics