Isa EbtehajHossein BonakdariMir Jafar Sadegh SafariBahram GharabaghiAmir Hossein ZajiHossien Riahi MadavarZohreh Sheikh KhozaniMohammad Sadegh Es-haghiAydin ShishegaranAli Danandeh MehrBonakdari, HosseinMehr, Ali DanandehSafari, Mir Jafar SadeghZaji, Amir HosseinGharabaghi, BahramMadavar, Hossien RiahiRiahi Madavar, HossienDanandeh Mehr, AliEbtehaj, Isa2025-10-0620201001-627910.1016/j.ijsrc.2019.08.0052-s2.0-85076473972http://dx.doi.org/10.1016/j.ijsrc.2019.08.005https://gcris.yasar.edu.tr/handle/123456789/7918https://doi.org/10.1016/j.ijsrc.2019.08.005Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence the limiting velocity should be determined to keep the channel bottom clean from sediment deposits. Recently sediment transport modeling using various artificial intelligence (AI) techniques has attracted the interest of many researchers. The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems. A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport. Utilizing one to seven dimensionless parameters 127 models are developed in the current study. In order to evaluate the different parameter combinations and select the training and testing data four strategies are considered. Considering the densimetric Froude number (Fr) as the dependent parameter a model with independent parameters of volumetric sediment concentration (C-V) and relative particle size (d/R) gave the best results with a mean absolute relative error (MARE) of 0.1 and a root means square error (RMSE) of 0.67. Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods. The percentage of the observed sample data bracketed by 95% predicted uncertainty bound (95PPU) is computed to assess the uncertainty of the best models. (C) 2019 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessNon-deposition, Sediment transport, Sensitivity analysis, Sewer, Uncertainty analysis, Urban drainageHYDRAULIC JUMP CHARACTERISTICS, PARTICLE SWARM OPTIMIZATION, EXTREME LEARNING-MACHINE, NON-DEPOSITION, BOUNDARY-CONDITIONS, DESIGN CRITERIA, PREDICTION, LIMIT, CHANNELS, PERFORMANCEUncertainty AnalysisSediment TransportSensitivity AnalysisNon-depositionUrban DrainageSewerArticle