Predicting seismic-induced liquefaction through ensemble learning frameworks

Abstract The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machin...

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Autores principales:	Mohammad H. Alobaidi, Mohamed A. Meguid, Fateh Chebana
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f079bd5f84ae41d085968e30cbed85ba
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id	oai:doaj.org-article:f079bd5f84ae41d085968e30cbed85ba
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spelling	oai:doaj.org-article:f079bd5f84ae41d085968e30cbed85ba2021-12-02T16:08:52ZPredicting seismic-induced liquefaction through ensemble learning frameworks10.1038/s41598-019-48044-02045-2322https://doaj.org/article/f079bd5f84ae41d085968e30cbed85ba2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-48044-0https://doaj.org/toc/2045-2322Abstract The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models which can be used as direct prediction methods to liquefaction occurrence. Ensemble learning is a recent advancement in this field. According to a predefined ensemble architecture, a number of learners are trained and their inferences are integrated to produce stable and improved generalization ability. However, there is a need to consider several aspects of the ensemble learning frameworks when exploiting them for a particular application; a comprehensive evaluation of an ensemble learner’s generalization ability is required but usually overlooked. Also, the literature falls short on work utilizing ensemble learning in liquefaction prediction. To this extent, this work examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A comprehensive analysis of fifteen ensemble models is performed. The results show improved prediction performance and diminishing uncertainty of ensembles, compared with single machine learning models.Mohammad H. AlobaidiMohamed A. MeguidFateh ChebanaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad H. Alobaidi Mohamed A. Meguid Fateh Chebana Predicting seismic-induced liquefaction through ensemble learning frameworks
description	Abstract The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models which can be used as direct prediction methods to liquefaction occurrence. Ensemble learning is a recent advancement in this field. According to a predefined ensemble architecture, a number of learners are trained and their inferences are integrated to produce stable and improved generalization ability. However, there is a need to consider several aspects of the ensemble learning frameworks when exploiting them for a particular application; a comprehensive evaluation of an ensemble learner’s generalization ability is required but usually overlooked. Also, the literature falls short on work utilizing ensemble learning in liquefaction prediction. To this extent, this work examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A comprehensive analysis of fifteen ensemble models is performed. The results show improved prediction performance and diminishing uncertainty of ensembles, compared with single machine learning models.
format	article
author	Mohammad H. Alobaidi Mohamed A. Meguid Fateh Chebana
author_facet	Mohammad H. Alobaidi Mohamed A. Meguid Fateh Chebana
author_sort	Mohammad H. Alobaidi
title	Predicting seismic-induced liquefaction through ensemble learning frameworks
title_short	Predicting seismic-induced liquefaction through ensemble learning frameworks
title_full	Predicting seismic-induced liquefaction through ensemble learning frameworks
title_fullStr	Predicting seismic-induced liquefaction through ensemble learning frameworks
title_full_unstemmed	Predicting seismic-induced liquefaction through ensemble learning frameworks
title_sort	predicting seismic-induced liquefaction through ensemble learning frameworks
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/f079bd5f84ae41d085968e30cbed85ba
work_keys_str_mv	AT mohammadhalobaidi predictingseismicinducedliquefactionthroughensemblelearningframeworks AT mohamedameguid predictingseismicinducedliquefactionthroughensemblelearningframeworks AT fatehchebana predictingseismicinducedliquefactionthroughensemblelearningframeworks
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Predicting seismic-induced liquefaction through ensemble learning frameworks

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