Browsing by Subject "Sediment transport"

Now showing 1 - 3 of 3

Open Access
Kernel ridge regression model for sediment transport in open channel flow
(Springer, 2021-01-11) Safari, M. J. S.; Arashloo, Shervin Rahimzadeh
Sediment transport modeling is of primary importance for the determination of channel design velocity in lined channels. This study proposes to model sediment transport in open channel flow using kernel ridge regression (KRR), a nonlinear regression technique formulated in the reproducing kernel Hilbert space. While the naïve kernel regression approach provides high flexibility for modeling purposes, the regularized variant is equipped with an additional mechanism for better generalization capability. In order to better tailor the KRR approach to the sediment transport modeling problem, unlike the conventional KRR approach, in this study the kernel parameter is directly learned from the data via a new gradient descent-based learning mechanism. Moreover, for model construction, a procedure based on Cholesky decomposition and forward-back substitution is applied to improve the computational complexity of the approach. Evaluation of the recommended technique is performed utilizing a large number of laboratory experimental data where the examination of the proposed approach in terms of three statistical performance indices for sediment transport modeling indicates a better performance for the developed model in particle Froude number computation, outperforming the conventional models as well as some other machine learning techniques.
Open Access
Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes
(Elsevier BV, 2023-08-30) Safari, M. J. S.; Rahimzadeh Arashloo, Shervin
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.
Open Access
Sparse kernel regression technique for self-cleansing channel design
(Elsevier Ltd, 2021-01) Safari, M. J.; Arashloo, Shervin Rahimzadeh
The application of a robust learning technique is inevitable in the development of a self-cleansing sediment transport model. This study addresses this problem and advocates the use of sparse kernel regression (SKR) technique to design a self-cleaning model. The SKR approach is a regression technique operating in the kernel space which also benefits from the desirable properties of a sparse solution. In order to develop a model applicable to a wide range of channel characteristics, five different experimental data sets from 14 different channels are utilized in this study. In this context, the efficacy of the SKR model is compared against the support vector regression (SVR) approach along with several other methods from the literature. According to the statistical analysis results, the SKR method is found to outperform the SVR and other regression equations. In particular, while empirical regression models fail to generate accurate results for other channel cross-section shapes and sizes, the SKR model provides promising results due to the inclusion of a channel parameter at the core of its structure and also by operating on an extensive range of experimental data. The superior efficacy of the SKR approach is also linked to its formulation in the kernel space while also benefiting from a sparse representation method to select the most useful training samples for model construction. As such, it also circumvent the requirement to evaluate irrelevant or noisy observations during the test phase of the model, and thus improving on the test phase running time.