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

作     者：Mir Jafar Sadegh Safari Shervin Rahimzadeh Arashloo Mir Jafar Sadegh Safari;Shervin Rahimzadeh Arashloo

作者机构：Department of Civil EngineeringYasar University Department of Computer EngineeringBilkent University 

出 版 物：《International Journal of Sediment Research》 (国际泥沙研究(英文版))

年 卷 期：2023年第38卷第6期

页      面：859-870页

核心收录：

学科分类：08[工学] 081502[工学-水力学及河流动力学] 0815[工学-水利工程] 

主　　题：Low-rank learning Multi-output regression Sediment transport Sewer flow Shear stress approach Velocity approach 

摘      要：The existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear *** 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 ***,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 *** efficiency of the developed method is examined and compared against the available conventional regression *** experimental results indicate that the RLRL model yields better results than its *** 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 *** 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.