Sparse kernel regression technique for self-cleansing channel design

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buir.contributor.authorArashloo, Shervin Rahimzadeh
buir.contributor.orcidArashloo, Shervin Rahimzadeh|0000-0003-0189-4774
dc.citation.epage101230-10en_US
dc.citation.spage101230-1en_US
dc.citation.volumeNumber47en_US
dc.contributor.authorSafari, M. J.
dc.contributor.authorArashloo, Shervin Rahimzadeh
dc.date.accessioned2022-01-24T08:33:18Z
dc.date.available2022-01-24T08:33:18Z
dc.date.issued2021-01
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThe 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.en_US
dc.description.provenanceSubmitted by Samet Emre (samet.emre@bilkent.edu.tr) on 2022-01-24T08:33:18Z No. of bitstreams: 1 Sparse_kernel_regression_technique_for_self-cleansing_channel_design.pdf: 2773282 bytes, checksum: 63b915ebdd2fb136e12458abdf154cab (MD5)en
dc.description.provenanceMade available in DSpace on 2022-01-24T08:33:18Z (GMT). No. of bitstreams: 1 Sparse_kernel_regression_technique_for_self-cleansing_channel_design.pdf: 2773282 bytes, checksum: 63b915ebdd2fb136e12458abdf154cab (MD5) Previous issue date: 2021-01en
dc.embargo.release2023-01-31
dc.identifier.doi10.1016/j.aei.2020.101230en_US
dc.identifier.issn1474-0346
dc.identifier.urihttp://hdl.handle.net/11693/76761
dc.language.isoEnglishen_US
dc.publisherElsevier Ltden_US
dc.relation.isversionofhttps://doi.org/10.1016/j.aei.2020.101230en_US
dc.source.titleAdvanced Engineering Informaticsen_US
dc.subjectMachine learningen_US
dc.subjectOpen channelen_US
dc.subjectSediment transporten_US
dc.subjectSelf-cleansingen_US
dc.subjectSparse kernel regressionen_US
dc.subjectSupport vector regressionen_US
dc.titleSparse kernel regression technique for self-cleansing channel designen_US
dc.typeArticleen_US

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