Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes  

作　　者：Mir Jafar Sadegh Safari Shervin Rahimzadeh Arashloo 

机构地区：[1]Department of Civil Engineering,Yas¸ar University,Izmir,Turkey [2]Department of Computer Engineering,Bilkent University,Ankara,Turkey

出　　处：《International Journal of Sediment Research》2023年第6期859-870,共12页国际泥沙研究（英文版）

摘　　要：The existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear stress.In the current study,incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning(RLRL)multi-output regression technique.Moreover,the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional shape.The efficiency of the developed method is examined and compared against the available conventional regression models.The experimental results indicate that the RLRL model yields better results than its counterparts.In particular,while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections,the proposed model provides satisfactory results for all channel shapes.The better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.

关 键 词：Low-rank learning Multi-output regression Sediment transport Sewer flow Shear stress approach Velocity approach 

分 类 号：TV142[水利工程—水力学及河流动力学]