Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping

Abstract Deterministic models have been widely applied in landslide risk assessment (LRA), but they have limitations in obtaining various geotechnical and hydraulic properties. The objective of this study is to suggest a new deterministic method based on machine learning (ML) algorithms. Eight cruci...

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Autores principales:	Dae-Hong Min, Hyung-Koo Yoon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/23486316c6a74f17ba1620bd2de21987
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spelling	oai:doaj.org-article:23486316c6a74f17ba1620bd2de219872021-12-02T16:36:04ZSuggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping10.1038/s41598-021-86137-x2045-2322https://doaj.org/article/23486316c6a74f17ba1620bd2de219872021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86137-xhttps://doaj.org/toc/2045-2322Abstract Deterministic models have been widely applied in landslide risk assessment (LRA), but they have limitations in obtaining various geotechnical and hydraulic properties. The objective of this study is to suggest a new deterministic method based on machine learning (ML) algorithms. Eight crucial variables of LRA are selected with reference to expert opinions, and the output value is set to the safety factor derived by Mohr–Coulomb failure theory in infinite slope. Linear regression and a neural network based on ML are applied to find the best model between independent and dependent variables. To increase the reliability of linear regression and the neural network, the results of back propagation, including gradient descent, Levenberg–Marquardt (LM), and Bayesian regularization (BR) methods, are compared. An 1800-item dataset is constructed through measured data and artificial data by using a geostatistical technique, which can provide the information of an unknown area based on measured data. The results of linear regression and the neural network show that the special LM and BR back propagation methods demonstrate a high determination of coefficient. The important variables are also investigated though random forest (RF) to overcome the number of various input variables. Only four variables—shear strength, soil thickness, elastic modulus, and fine content—demonstrate a high reliability for LRA. The results show that it is possible to perform LRA with ML, and four variables are enough when it is difficult to obtain various variables.Dae-Hong MinHyung-Koo YoonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-24 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dae-Hong Min Hyung-Koo Yoon Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
description	Abstract Deterministic models have been widely applied in landslide risk assessment (LRA), but they have limitations in obtaining various geotechnical and hydraulic properties. The objective of this study is to suggest a new deterministic method based on machine learning (ML) algorithms. Eight crucial variables of LRA are selected with reference to expert opinions, and the output value is set to the safety factor derived by Mohr–Coulomb failure theory in infinite slope. Linear regression and a neural network based on ML are applied to find the best model between independent and dependent variables. To increase the reliability of linear regression and the neural network, the results of back propagation, including gradient descent, Levenberg–Marquardt (LM), and Bayesian regularization (BR) methods, are compared. An 1800-item dataset is constructed through measured data and artificial data by using a geostatistical technique, which can provide the information of an unknown area based on measured data. The results of linear regression and the neural network show that the special LM and BR back propagation methods demonstrate a high determination of coefficient. The important variables are also investigated though random forest (RF) to overcome the number of various input variables. Only four variables—shear strength, soil thickness, elastic modulus, and fine content—demonstrate a high reliability for LRA. The results show that it is possible to perform LRA with ML, and four variables are enough when it is difficult to obtain various variables.
format	article
author	Dae-Hong Min Hyung-Koo Yoon
author_facet	Dae-Hong Min Hyung-Koo Yoon
author_sort	Dae-Hong Min
title	Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
title_short	Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
title_full	Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
title_fullStr	Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
title_full_unstemmed	Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
title_sort	suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/23486316c6a74f17ba1620bd2de21987
work_keys_str_mv	AT daehongmin suggestionforanewdeterministicmodelcoupledwithmachinelearningtechniquesforlandslidesusceptibilitymapping AT hyungkooyoon suggestionforanewdeterministicmodelcoupledwithmachinelearningtechniquesforlandslidesusceptibilitymapping
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Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping

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