Citation

Alobaidi MH, Meguid MA, Chebana F. Sci. Rep. 2019; 9(1): e11786.

Affiliation

Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Québec, QC, G1K 9A9, Canada.

Copyright

(Copyright © 2019, Nature Publishing Group)

DOI

10.1038/s41598-019-48044-0

PMID

31409827

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.

Language: en