Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes

Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 30; H. 14; S. 39637 - 39652
Hauptverfasser:	Kouzehkalani Sales, Ali, Gul, Enes, Safari, Mir Jafar Sadegh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023 Springer Nature B.V
Schlagworte:	Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Design Diameters Earth and Environmental Science Ecotoxicology Education, Distance Environment Environmental Chemistry Environmental Health Environmental Pollution Environmental science Learning algorithms Machine Learning Modelling Neural Networks, Computer Outliers (statistics) Parameters Pipes pollution Pollution dispersion Pollution transport Research Article Robustness sediment deposition Sediment transport sediments Sewer pipes Sewer systems Thickness uncertainty Waste Water Technology Water Management Water Pollution Control Online sequential Parallel layer perceptron Extreme learning machine Outlier robust Deposited bed Sediment transport
ISSN:	1614-7499, 0944-1344, 1614-7499
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Abstract	Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness ( t s ) or deposited bed width ( W b ) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t s and W b can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W b / Y , t s / Y , W b / D , and t s / D ( Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W b /Y , t s /Y , W/D , and t s /D are considered for model development. It is found that models that incorporate sediment bed thickness ( t s ) provide better results than those which use deposited bed width ( W b ) in their structures. Among four different scenarios, models that utilized t s /D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.
AbstractList	Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (t ) or deposited bed width (W ) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t and W can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W /Y, t /Y, W /D, and t /D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W /Y, t /Y, W/D, and t /D are considered for model development. It is found that models that incorporate sediment bed thickness (t ) provide better results than those which use deposited bed width (W ) in their structures. Among four different scenarios, models that utilized t /D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes. Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (tₛ) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of tₛ and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, tₛ/Y, Wb/D, and tₛ/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, tₛ/Y, W/D, and tₛ/D are considered for model development. It is found that models that incorporate sediment bed thickness (tₛ) provide better results than those which use deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized tₛ/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes. Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness ( t s ) or deposited bed width ( W b ) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t s and W b can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W b / Y , t s / Y , W b / D , and t s / D ( Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W b /Y , t s /Y , W/D , and t s /D are considered for model development. It is found that models that incorporate sediment bed thickness ( t s ) provide better results than those which use deposited bed width ( W b ) in their structures. Among four different scenarios, models that utilized t s /D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes. Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (ts) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of ts and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, ts/Y, Wb/D, and ts/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, ts/Y, W/D, and ts/D are considered for model development. It is found that models that incorporate sediment bed thickness (ts) provide better results than those which use deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized ts/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (ts) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of ts and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, ts/Y, Wb/D, and ts/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, ts/Y, W/D, and ts/D are considered for model development. It is found that models that incorporate sediment bed thickness (ts) provide better results than those which use deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized ts/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes. Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (ts) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of ts and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, ts/Y, Wb/D, and ts/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, ts/Y, W/D, and ts/D are considered for model development. It is found that models that incorporate sediment bed thickness (ts) provide better results than those which use deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized ts/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.
Author	Safari, Mir Jafar Sadegh Kouzehkalani Sales, Ali Gul, Enes
Author_xml	– sequence: 1 givenname: Ali surname: Kouzehkalani Sales fullname: Kouzehkalani Sales, Ali organization: Department of Civil Engineering, Elm-O-Fan University College of Science and Technology – sequence: 2 givenname: Enes surname: Gul fullname: Gul, Enes organization: Department of Civil Engineering, Inonu University – sequence: 3 givenname: Mir Jafar Sadegh orcidid: 0000-0003-0559-5261 surname: Safari fullname: Safari, Mir Jafar Sadegh email: jafar.safari@yasar.edu.tr organization: Department of Civil Engineering, Yaşar University
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Cites_doi	10.1016/j.ijsrc.2017.05.007 10.1016/j.ijsrc.2019.08.005 10.1016/S0925-2312(03)00440-5 10.1016/j.neucom.2005.12.126 10.1016/j.measurement.2016.06.042 10.2166/wst.1996.0210 10.1016/j.neucom.2016.11.040 10.1002/wer.1037 10.1016/j.neucom.2015.04.018 10.1109/TII.2018.2854549 10.1109/TNN.2006.880583 10.1016/j.ijsrc.2018.04.007 10.1016/j.enconman.2018.10.089 10.1016/j.catena.2021.105791 10.1109/COMST.2018.2812301 10.1061/(ASCE)0733-9429(2003)129:4(291) 10.2166/wst.2019.106 10.1007/s13042-011-0019-y 10.1061/(ASCE)PS.1949-1204.0000449 10.1007/s11269-014-0774-0 10.1016/j.neucom.2014.09.022 10.2307/2532051 10.2166/wst.2020.154 10.1007/s10652-017-9550-z 10.1109/JSYST.2016.2550530 10.2166/wst.2017.429 10.2166/wst.2017.267 10.1109/TNNLS.2015.2424995 10.1061/(ASCE)0733-9429(2003)129:4(276) 10.2166/wst.1994.0658 10.1080/00221686.2020.1780501 10.2166/wst.1997.0654 10.1061/(ASCE)PS.1949-1204.0000335
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Keywords	Online sequential Parallel layer perceptron Extreme learning machine Outlier robust Deposited bed Sediment transport
Language	English
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References	SafariMJSShirzadAMohammadiMSediment transport modeling in deposited bed sewers: unified form of May’s equations using the Particles warm optimization algorithmWater Sci Technol20177649921000 WuJGuoSLiJZengDBig data meet green challenges: big data toward green applicationsIEEE Syst J2016103888900 EbtehajIBonakdariHPerformance evaluation of adaptive neural fuzzy inference system for sediment transport in sewersWater Resour Manag2014281347654779 MayRWPSediment transport in pipes and sewers with deposited beds (technical report)1993WallingfordHydraulic Research Ltd LiuZ-FLiL-LTsengM-LLimMKPrediction short-term photovoltaic power using improved chicken swarm optimizer-extreme learning machine modelJ Clean Prod2020248 PerrusquiaGSSediment transport in 486 pipe channels (Report B: 55)1992SwedenChalmers University of Technology HenríquezPARuzGAExtreme learning machine with a deterministic assignment of hidden weights in two parallel layersNeurocomputing2017226109116 SafariMJSDanandehMehrAMultigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed depositInt J Sediment Res2018333262270 Craven JP (1953) “The transportation of sand in pipes—Full pipe flow.” In Proc., 5th Hydraulics Conf. Ames, IA: Iowa State Univ Ambrose HH (1953) “The transportation of sand in pipes free surface flow.” In Proc., 5th Hydraulic Conf., Bulletin 34, State University of Iowa Studies in Engineering. Ames, IA: Iowa State Univ SafariMJSMohammadiMAb GhaniAExperimental studies of self-cleansing drainage system design: a reviewJ Pipeline Syst Eng20189404018017 WuJGuoSHuangHLiuWXiangYInformation and communications technologies for sustainable development goals: state-of-the-art, needs and perspectivesIEEE Communications Surveys & Tutorials201820323892406 NalluriCEl-ZaemeyAKChanHLSediment transport over fixed deposited beds in sewers-an appraisal of existing modelsWater Sci Technol1997368123128 Lawrence I, Lin K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 255–268 OtaJJNalluriCUrban storm sewer design: approach in consideration of sedimentsJ Hydraul Eng20031294291297 EbtehajIBonakdariHSafariMJSGharabaghiBZajiAHMadavarHRKhozaniZSEs-haghiMSShishegaranAMehrADCombination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipesInt J Sedim Res2020352157170 YadavBChSMathurSAdamowskiJDischarge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, GermanyMeasurement201692433445 ZhangKLuoMOutlier-robust extreme learning machine for regression problemsNeurocomputing201515115191527 HuangG-BZhuQ-YSiewC-KExtreme learning machine: theory and applicationsNeurocomputing2006701–3489501 Perrusquia GS (1991) “Bed load transport in storm sewers: steam traction in pipe channels.” Ph.D. thesis, Dept. of Civil Engineering, Chalmers Univ. of Technology ButlerDMayRAckersJSelf-cleansing 442 sewer design based on sediment transport principlesJ Hydraul Eng20031294276282 SafariMJSDecision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipesWater Sci Technol201979611131122 ZhaoDWangJZhaoXTriantafilisJClay content mapping and uncertainty estimation using weighted model averagingCATENA2022209 Safari MJS (2016) Self-cleansing drainage system design by incipient motion and incipient deposition-based models (Doctoral dissertation, PhD Thesis, Istanbul Technical University, Turkey) LiL-LLiuZ-FTsengM-LJantarakolicaKLimMKUsing enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind powerExpert Syst Appl2021184 Danandeh MehrASafariMJSApplication of soft computing techniques for particle Froude number estimation in sewer pipesJ Pipeline Syst Eng202011204020002 ZhangDPengXPanKLiuYA novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machineEnergy Convers Manage2019180338357 LuoXSunJWangLWangWZhaoWWuJWangJHZhangZShort-term wind speed forecasting via stacked extreme learning machine with generalized correntropyIEEE Trans Industr Inf2018141149634971 SafariMJSShirzadASelf- cleansing design of sewers: definition of the optimum deposited bed thicknessWater Environ Res20199154074161:CAS:528:DC%2BC1MXns1OqtLw%3D El-ZaemeyAKSSediment transport over deposited beds in sewers (doctoral dissertation)1991U.K.University of Newcastle upon Tyne SafariMJSAksoyHExperimental analysis for self-cleansing open channel designJ Hydraul Res2021593500511 Ackers P (1991) Sediment aspects of drainage and outfall design. In Proc., Int. Symp. on Environmental Hydraulics, Rotterdam, Netherlands: A.A. Balkema SafariMJSAksoyHUnalNEMohammadiMExperimental analysis of sediment incipient motion in rigid boundary open channelsEnviron Fluid Mech2017176128112981:CAS:528:DC%2BC2sXhslWrsbvE TangJDengCHuangG-BExtreme learning machine for multilayer perceptronIEEE Transactions on Neural Networks and Learning Systems2015274809821 TavaresLDSaldanhaRRVieiraDAGExtreme learning machine with parallel layer perceptronsNeurocomputing2015166164171 PerrusquiaGSAn experimental study from flume to stream traction in pipe channels (ReportB57)1993SwedenChalmers University of Technology LiangN-YHuangG-BSaratchandranPSundararajanNA fast and accurate online sequential learning algorithm for feedforward networksIEEE Trans Neural Networks200617614111423 CaminhasWMVieiraDAGVasconcelosJAParallel layer perceptronNeurocomputing2003553–4771778 MontesCVanegasSKapelanZBerardiLSaldarriagaJNon-deposition self-cleansing models for large sewer pipesWater Sci Technol2020813606621 Ab GhaniASediment transport in sewers (doctoral dissertation)1993U.K.University of Newcastle upon Tyne Ackers JC, Butler D, May RWP (1996) Design of sewers to control sediment problems. Construction Industry Research and Information Association (CIRIA), London, pp. 1e181. Rep.No.141, London AlvarezEMThe influence cohesion on sediment movement in channels of circular cross-section (doctoral dissertation)1990U.K.University of Newcastle upon Tyne NalluriCAb GhaniAEl-ZaemeyAKSSediment transport over deposited beds in sewersWater Sci Technol1994291e2125133 MayRWPAckersJCButlerDJohnSDevelopment of design methodology for self-cleansing sewersWater Sci Technol1996339195205 May RWP, Brown PM, Hare GR, Jones KD (1989) Self-cleansing conditions for sewers carrying sediment. Rep. No. SR 221. Wallingford, Oxfordshire: Hydraulics Research Ltd. HuangG-BWangDHLanYExtreme learning machines: a surveyInt J Mach Learn Cybern201122107122 SafariMJSMohammadiBKargarKInvasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed depositJ Clean Prod2020276 RoushangarKGhasempourREstimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithmInt J Sediment Res2017324564574 Ackers P (1984) Sediment transport in sewers and the design implications. In Proc., Int. Conf. on Planning, Construction, Maintenance, and Operation of Sewerage Systems, 215–230. Reading, UK: BHRA/WRc AksoyHSafariMJSUnalNEMohammadiMVelocity-based analysis of sediment incipient deposition in rigid boundary open channelsWater Sci Technol201776925352543 MJS Safari (24989_CR35) 2021; 59 N-Y Liang (24989_CR20) 2006; 17 JJ Ota (24989_CR29) 2003; 129 24989_CR25 Z-F Liu (24989_CR21) 2020; 248 MJS Safari (24989_CR41) 2020; 276 A Danandeh Mehr (24989_CR11) 2020; 11 MJS Safari (24989_CR40) 2018; 9 LD Tavares (24989_CR44) 2015; 166 K Roushangar (24989_CR33) 2017; 32 D Zhao (24989_CR50) 2022; 209 WM Caminhas (24989_CR9) 2003; 55 I Ebtehaj (24989_CR12) 2014; 28 MJS Safari (24989_CR39) 2017; 76 J Wu (24989_CR45) 2016; 10 B Yadav (24989_CR47) 2016; 92 EM Alvarez (24989_CR6) 1990 MJS Safari (24989_CR38) 2017; 17 24989_CR32 D Butler (24989_CR8) 2003; 129 PA Henríquez (24989_CR15) 2017; 226 MJS Safari (24989_CR37) 2019; 91 H Aksoy (24989_CR5) 2017; 76 AKS El-Zaemey (24989_CR14) 1991 RWP May (24989_CR24) 1996; 33 MJS Safari (24989_CR36) 2018; 33 X Luo (24989_CR22) 2018; 14 G-B Huang (24989_CR17) 2011; 2 A Ab Ghani (24989_CR1) 1993 24989_CR42 C Nalluri (24989_CR27) 1994; 29 J Tang (24989_CR43) 2015; 27 GS Perrusquia (24989_CR31) 1993 K Zhang (24989_CR48) 2015; 151 24989_CR7 C Montes (24989_CR26) 2020; 81 24989_CR2 24989_CR18 24989_CR3 24989_CR4 G-B Huang (24989_CR16) 2006; 70 D Zhang (24989_CR49) 2019; 180 RWP May (24989_CR23) 1993 24989_CR10 L-L Li (24989_CR19) 2021; 184 GS Perrusquia (24989_CR30) 1992 MJS Safari (24989_CR34) 2019; 79 C Nalluri (24989_CR28) 1997; 36 J Wu (24989_CR46) 2018; 20 I Ebtehaj (24989_CR13) 2020; 35
References_xml	– reference: LiL-LLiuZ-FTsengM-LJantarakolicaKLimMKUsing enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind powerExpert Syst Appl2021184 – reference: SafariMJSAksoyHExperimental analysis for self-cleansing open channel designJ Hydraul Res2021593500511 – reference: Ambrose HH (1953) “The transportation of sand in pipes free surface flow.” In Proc., 5th Hydraulic Conf., Bulletin 34, State University of Iowa Studies in Engineering. Ames, IA: Iowa State Univ – reference: ZhaoDWangJZhaoXTriantafilisJClay content mapping and uncertainty estimation using weighted model averagingCATENA2022209 – reference: MayRWPAckersJCButlerDJohnSDevelopment of design methodology for self-cleansing sewersWater Sci Technol1996339195205 – reference: PerrusquiaGSAn experimental study from flume to stream traction in pipe channels (ReportB57)1993SwedenChalmers University of Technology – reference: AksoyHSafariMJSUnalNEMohammadiMVelocity-based analysis of sediment incipient deposition in rigid boundary open channelsWater Sci Technol201776925352543 – reference: LuoXSunJWangLWangWZhaoWWuJWangJHZhangZShort-term wind speed forecasting via stacked extreme learning machine with generalized correntropyIEEE Trans Industr Inf2018141149634971 – reference: YadavBChSMathurSAdamowskiJDischarge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, GermanyMeasurement201692433445 – reference: AlvarezEMThe influence cohesion on sediment movement in channels of circular cross-section (doctoral dissertation)1990U.K.University of Newcastle upon Tyne – reference: ZhangDPengXPanKLiuYA novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machineEnergy Convers Manage2019180338357 – reference: WuJGuoSHuangHLiuWXiangYInformation and communications technologies for sustainable development goals: state-of-the-art, needs and perspectivesIEEE Communications Surveys & Tutorials201820323892406 – reference: Safari MJS (2016) Self-cleansing drainage system design by incipient motion and incipient deposition-based models (Doctoral dissertation, PhD Thesis, Istanbul Technical University, Turkey) – reference: HenríquezPARuzGAExtreme learning machine with a deterministic assignment of hidden weights in two parallel layersNeurocomputing2017226109116 – reference: NalluriCAb GhaniAEl-ZaemeyAKSSediment transport over deposited beds in sewersWater Sci Technol1994291e2125133 – reference: Perrusquia GS (1991) “Bed load transport in storm sewers: steam traction in pipe channels.” Ph.D. thesis, Dept. of Civil Engineering, Chalmers Univ. of Technology – reference: SafariMJSAksoyHUnalNEMohammadiMExperimental analysis of sediment incipient motion in rigid boundary open channelsEnviron Fluid Mech2017176128112981:CAS:528:DC%2BC2sXhslWrsbvE – reference: WuJGuoSLiJZengDBig data meet green challenges: big data toward green applicationsIEEE Syst J2016103888900 – reference: Ab GhaniASediment transport in sewers (doctoral dissertation)1993U.K.University of Newcastle upon Tyne – reference: SafariMJSMohammadiMAb GhaniAExperimental studies of self-cleansing drainage system design: a reviewJ Pipeline Syst Eng20189404018017 – reference: OtaJJNalluriCUrban storm sewer design: approach in consideration of sedimentsJ Hydraul Eng20031294291297 – reference: ButlerDMayRAckersJSelf-cleansing 442 sewer design based on sediment transport principlesJ Hydraul Eng20031294276282 – reference: El-ZaemeyAKSSediment transport over deposited beds in sewers (doctoral dissertation)1991U.K.University of Newcastle upon Tyne – reference: NalluriCEl-ZaemeyAKChanHLSediment transport over fixed deposited beds in sewers-an appraisal of existing modelsWater Sci Technol1997368123128 – reference: Ackers P (1991) Sediment aspects of drainage and outfall design. In Proc., Int. Symp. on Environmental Hydraulics, Rotterdam, Netherlands: A.A. Balkema – reference: ZhangKLuoMOutlier-robust extreme learning machine for regression problemsNeurocomputing201515115191527 – reference: Craven JP (1953) “The transportation of sand in pipes—Full pipe flow.” In Proc., 5th Hydraulics Conf. Ames, IA: Iowa State Univ – reference: MayRWPSediment transport in pipes and sewers with deposited beds (technical report)1993WallingfordHydraulic Research Ltd – reference: SafariMJSShirzadAMohammadiMSediment transport modeling in deposited bed sewers: unified form of May’s equations using the Particles warm optimization algorithmWater Sci Technol20177649921000 – reference: EbtehajIBonakdariHPerformance evaluation of adaptive neural fuzzy inference system for sediment transport in sewersWater Resour Manag2014281347654779 – reference: HuangG-BZhuQ-YSiewC-KExtreme learning machine: theory and applicationsNeurocomputing2006701–3489501 – reference: Lawrence I, Lin K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 255–268 – reference: SafariMJSDanandehMehrAMultigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed depositInt J Sediment Res2018333262270 – reference: LiuZ-FLiL-LTsengM-LLimMKPrediction short-term photovoltaic power using improved chicken swarm optimizer-extreme learning machine modelJ Clean Prod2020248 – reference: SafariMJSShirzadASelf- cleansing design of sewers: definition of the optimum deposited bed thicknessWater Environ Res20199154074161:CAS:528:DC%2BC1MXns1OqtLw%3D – reference: Danandeh MehrASafariMJSApplication of soft computing techniques for particle Froude number estimation in sewer pipesJ Pipeline Syst Eng202011204020002 – reference: EbtehajIBonakdariHSafariMJSGharabaghiBZajiAHMadavarHRKhozaniZSEs-haghiMSShishegaranAMehrADCombination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipesInt J Sedim Res2020352157170 – reference: SafariMJSMohammadiBKargarKInvasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed depositJ Clean Prod2020276 – reference: SafariMJSDecision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipesWater Sci Technol201979611131122 – reference: MontesCVanegasSKapelanZBerardiLSaldarriagaJNon-deposition self-cleansing models for large sewer pipesWater Sci Technol2020813606621 – reference: RoushangarKGhasempourREstimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithmInt J Sediment Res2017324564574 – reference: CaminhasWMVieiraDAGVasconcelosJAParallel layer perceptronNeurocomputing2003553–4771778 – reference: May RWP, Brown PM, Hare GR, Jones KD (1989) Self-cleansing conditions for sewers carrying sediment. Rep. No. SR 221. Wallingford, Oxfordshire: Hydraulics Research Ltd. – reference: PerrusquiaGSSediment transport in 486 pipe channels (Report B: 55)1992SwedenChalmers University of Technology – reference: LiangN-YHuangG-BSaratchandranPSundararajanNA fast and accurate online sequential learning algorithm for feedforward networksIEEE Trans Neural Networks200617614111423 – reference: HuangG-BWangDHLanYExtreme learning machines: a surveyInt J Mach Learn Cybern201122107122 – reference: Ackers JC, Butler D, May RWP (1996) Design of sewers to control sediment problems. Construction Industry Research and Information Association (CIRIA), London, pp. 1e181. Rep.No.141, London – reference: TangJDengCHuangG-BExtreme learning machine for multilayer perceptronIEEE Transactions on Neural Networks and Learning Systems2015274809821 – reference: TavaresLDSaldanhaRRVieiraDAGExtreme learning machine with parallel layer perceptronsNeurocomputing2015166164171 – reference: Ackers P (1984) Sediment transport in sewers and the design implications. In Proc., Int. Conf. on Planning, Construction, Maintenance, and Operation of Sewerage Systems, 215–230. Reading, UK: BHRA/WRc – volume: 32 start-page: 564 issue: 4 year: 2017 ident: 24989_CR33 publication-title: Int J Sediment Res doi: 10.1016/j.ijsrc.2017.05.007 – volume-title: Sediment transport in sewers (doctoral dissertation) year: 1993 ident: 24989_CR1 – volume: 35 start-page: 157 issue: 2 year: 2020 ident: 24989_CR13 publication-title: Int J Sedim Res doi: 10.1016/j.ijsrc.2019.08.005 – volume: 55 start-page: 771 issue: 3–4 year: 2003 ident: 24989_CR9 publication-title: Neurocomputing doi: 10.1016/S0925-2312(03)00440-5 – volume: 70 start-page: 489 issue: 1–3 year: 2006 ident: 24989_CR16 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 92 start-page: 433 year: 2016 ident: 24989_CR47 publication-title: Measurement doi: 10.1016/j.measurement.2016.06.042 – volume: 33 start-page: 195 issue: 9 year: 1996 ident: 24989_CR24 publication-title: Water Sci Technol doi: 10.2166/wst.1996.0210 – ident: 24989_CR2 – volume: 226 start-page: 109 year: 2017 ident: 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SubjectTerms	Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Design Diameters Earth and Environmental Science Ecotoxicology Education, Distance Environment Environmental Chemistry Environmental Health Environmental Pollution Environmental science Learning algorithms Machine Learning Modelling Neural Networks, Computer Outliers (statistics) Parameters Pipes pollution Pollution dispersion Pollution transport Research Article Robustness sediment deposition Sediment transport sediments Sewer pipes Sewer systems Thickness uncertainty Waste Water Technology Water Management Water Pollution Control
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Title	Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes
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