Extreme learning machine assessment for estimating sediment transport in open channels. (October 2016)

• Record Type: Journal Article
• Authors: Ebtehaj, Isa; Bonakdari, Hossein; Shamshirband, Shahaboddin
• Is Part Of: Engineering with computers. Volume 32:Number 4(2016)
• Page Start: 691
• Page End: 704
• Publication Date: 2016-10
• Subjects: Bed load -- Extreme learning machines (ELM) -- Limit of deposition -- Sediment transport -- Storm water; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-016-0446-1
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract 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.