Sparse kernel regression technique for self-cleansing channel design. (January 2021)

• Record Type: Journal Article
• Authors: Safari, Mir Jafar Sadegh; Rahimzadeh Arashloo, Shervin
• Is Part Of: Advanced engineering informatics. Volume 47(2021)
• Publication Date: 2021-01
• Subjects: Machine learning -- Open channel -- Sediment transport -- Self-cleansing -- Sparse kernel regression -- Support vector regression; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101230
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Sparse kernel regression (SKR) is implemented for channel design. SKR is recommended for environmental modeling for the first time. Model parameters are selected based on sediment transport hydraulics. Kernel-based support vector regression (SVR) method is considered as a benchmark. SKR outperforms SVR and existing regression formulae. Abstract: 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 circumvents the requirement to evaluate irrelevant or noisy observations during the test phase of the model, and thus improving on the test phase running time.