Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes  

作　　者：Isa Ebtehaj Hossein Bonakdari Mir Jafar Sadegh Safari Bahram Gharabaghi Amir Hossein Zaji Hossien Riahi Madavar Zohreh Sheikh Khozani Mohammad Sadegh Es-haghi Aydin Shishegaran Ali Danandeh Mehr 

机构地区：[1]Department of Civil Engineering,Razi University,Kermanshah,Iran [2]Environmental Research Center,Razi University,Kermanshah,Iran [3]Department of Civil Engineering,Yasar University,Izmir,Turkey [4]School of Engineering,University of Guelph,Guelph,Ontario,NIG 2W1,Canada [5]Department of Water Engineering,Vali-e-Asr University of Rafsanjan,Rafsanjan,Iran [6]School of Civil Engineering,K.N.Toosi University of Technology,Tehran,Iran [7]Department of Water,and Environmental,Iran University of Science and Technology,Tehran,Iran [8]Department of Civil Engineering,Antalya Bilim University,Antalya,Turkey [9]Smart and Sustainable Township Research Center,Faculty of Engineering&Built Environment,Universiti Kebangsaan Malaysia,Bangi,Selangor,43600 UKM,Malaysia

出　　处：《International Journal of Sediment Research》2020年第2期157-170,共14页国际泥沙研究（英文版）

摘　　要：Mitigation 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(CV)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.Mitigation 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 co mbinations and select the training and te sting 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 by95% predicted uncertainty bound(95 PPU) is computed to assess the uncertainty of the best models.

关 键 词：Non-deposition Sediment transport Sensitivity analysis SEWER Uncertainty analysis Urban drainage 

分 类 号：TV149[水利工程—水力学及河流动力学]