Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization

Abstract The assessment of loess slope stability is a highly complex nonlinear problem. There are many factors that influence the stability of loess slopes. Some of them have the characteristic of uncertainty. Meanwhile, the relationship between different factors may be complicated. The existence of...

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Autor principal:	Bin Gong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3711f65353642a584aa813f6ade6d9d
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spelling	oai:doaj.org-article:a3711f65353642a584aa813f6ade6d9d2021-12-02T18:03:06ZStudy of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization10.1038/s41598-021-97484-02045-2322https://doaj.org/article/a3711f65353642a584aa813f6ade6d9d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97484-0https://doaj.org/toc/2045-2322Abstract The assessment of loess slope stability is a highly complex nonlinear problem. There are many factors that influence the stability of loess slopes. Some of them have the characteristic of uncertainty. Meanwhile, the relationship between different factors may be complicated. The existence of multiple correlation will affect the objectivity of stability analysis and prevent the model from making correct judgments. In this paper, the main factors affecting the stability of loess slopes are analyzed by means of the partial least-squares regression (PLSR). After that, two new synthesis variables with better interpretation to the dependent variables are extracted. By this way, the multicollinearity among variables is overcome preferably. Moreover, the BP neural network is further used to determine the nonlinear relationship between the new components and the slope safety factor. Then, a new improved BP model based on the partial least-squares regression, which is initialized by the particle swarm optimization (PSO) algorithm, is developed, i.e., the PLSR-BP model. The network with global convergence capability is simplified and more efficient. The test results of the model show satisfactory precision, which indicates that the model is feasible and effective for stability evaluation of loess slopes.Bin GongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bin Gong Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
description	Abstract The assessment of loess slope stability is a highly complex nonlinear problem. There are many factors that influence the stability of loess slopes. Some of them have the characteristic of uncertainty. Meanwhile, the relationship between different factors may be complicated. The existence of multiple correlation will affect the objectivity of stability analysis and prevent the model from making correct judgments. In this paper, the main factors affecting the stability of loess slopes are analyzed by means of the partial least-squares regression (PLSR). After that, two new synthesis variables with better interpretation to the dependent variables are extracted. By this way, the multicollinearity among variables is overcome preferably. Moreover, the BP neural network is further used to determine the nonlinear relationship between the new components and the slope safety factor. Then, a new improved BP model based on the partial least-squares regression, which is initialized by the particle swarm optimization (PSO) algorithm, is developed, i.e., the PLSR-BP model. The network with global convergence capability is simplified and more efficient. The test results of the model show satisfactory precision, which indicates that the model is feasible and effective for stability evaluation of loess slopes.
format	article
author	Bin Gong
author_facet	Bin Gong
author_sort	Bin Gong
title	Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
title_short	Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
title_full	Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
title_fullStr	Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
title_full_unstemmed	Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization
title_sort	study of plsr-bp model for stability assessment of loess slope based on particle swarm optimization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a3711f65353642a584aa813f6ade6d9d
work_keys_str_mv	AT bingong studyofplsrbpmodelforstabilityassessmentofloessslopebasedonparticleswarmoptimization
_version_	1718378827449630720

Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization

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