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. 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 com

SUBMITTER: Alobaidi MH 

PROVIDER: S-EPMC6692379 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature