Browsing by Subject "Open channel"

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
Lq-norm multiple kernel fusion regression for self-cleansing sediment transport
(Springer Dordrecht, 2024-02-02) Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Gargari, Mehrnoush Kohandel
Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section, while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m, a width of 0.6 m, a depth of 0.7 m and having an automatic control system for regulating channel slope, discharge and sediment rate. Behind utilizing collected experimental data in this study, existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure, this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental, hydrological and hydraulic problems.
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.