Journal
ENGINEERING WITH COMPUTERS
Volume 32, Issue 4, Pages 691-704Publisher
SPRINGER
DOI: 10.1007/s00366-016-0446-1
Keywords
Bed load; Extreme learning machines (ELM); Limit of deposition; Sediment transport; Storm water
Ask authors/readers for more resources
The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including movement, transport, sediment, transport mode, and flow resistance. Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available