Mir Jafar Sadegh SafariSafari, Mir Jafar Sadegh2025-10-0620199781843395843, 0080336590, 0080310362, 9781843395959, 9781843396000, 9780080336695, 0080290930, 9781843391883, 0080304362, 978184339148702731223, 199697320273-12231996-973210.2166/wst.2019.1062-s2.0-85065777183https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065777183&doi=10.2166%2Fwst.2019.106&partnerID=40&md5=0573fc576b0a632a7d88777695610721https://gcris.yasar.edu.tr/handle/123456789/9430https://doi.org/10.2166/wst.2019.106Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates the bed load sediment transport in sewer pipes with particular reference to the non-deposition condition in clean bed channels. Four data sets available in the literature covering wide ranges of pipe size sediment size and sediment volumetric concentration have been utilized through applying decision tree (DT) generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) techniques for modeling. The developed models have been compared with conventional regression models available in the literature. The model performance indicators showed that DT GR and MARS models outperform conventional regression models. Result shows that GR and MARS models are comparable in terms of calculating particle Froude number and performing better than DT. It is concluded that conventional regression models generally overestimate particle Froude number for the non-deposition condition of sediment transport while DT GR and MARS outputs are close to their measured counterparts. © 2019 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessDecision Tree, Generalized Regression, Multivariate Adaptive Regression Splines, Sediment Transport, Self-cleansing, Sewer, Data Mining, Decision Trees, Drainage, Froude Number, Neural Networks, Sediment Transport, Sedimentation, Sewers, Trees (mathematics), Deposition Conditions, Generalized Regression, Generalized Regression Neural Networks, Multivariate Adaptive Regression Splines, Sediment Deposition, Self-cleansing, Urban Drainage Systems, Volumetric Concentrations, Regression Analysis, Artificial Neural Network, Bedload, Decision Analysis, Model, Modeling, Multiple Regression, Sediment Transport, Sewer Network, Urban Drainage, Article, Cleaning, Decision Tree, Sewer, Analysis, Multivariate Analysis, Sanitation, Sediment, Sewage, Statistical Model, Statistics And Numerical Data, Water Pollution, Decision Trees, Drainage Sanitary, Geologic Sediments, Models Statistical, Multivariate Analysis, Neural Networks (computer), Waste Disposal Fluid, Water PollutionData mining, Decision trees, Drainage, Froude number, Neural networks, Sediment transport, Sedimentation, Sewers, Trees (mathematics), Deposition conditions, Generalized regression, Generalized regression neural networks, Multivariate adaptive regression splines, Sediment deposition, Self-cleansing, Urban drainage systems, Volumetric concentrations, Regression analysis, artificial neural network, bedload, decision analysis, model, modeling, multiple regression, sediment transport, sewer network, urban drainage, article, cleaning, decision tree, sewer, analysis, multivariate analysis, sanitation, sediment, sewage, statistical model, statistics and numerical data, water pollution, Decision Trees, Drainage Sanitary, Geologic Sediments, Models Statistical, Multivariate Analysis, Neural Networks (Computer), Waste Disposal Fluid, Water PollutionDecision TreeGeneralized RegressionMultivariate Adaptive Regression SplinesSediment TransportSelf-cleansingSewerArticle