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Open AccessArticle

Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

Division of Computer Science, Pablo de Olavide University, Seville ES-41013, Spain
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Academic Editor: César M. A. Vasques
Appl. Sci. 2017, 7(6), 625; https://doi.org/10.3390/app7060625
Received: 19 April 2017 / Revised: 31 May 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy. View Full-Text
Keywords: imbalanced classification; ensemble learning; large earthquake prediction imbalanced classification; ensemble learning; large earthquake prediction
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Fernández-Gómez, M.J.; Asencio-Cortés, G.; Troncoso, A.; Martínez-Álvarez, F. Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning. Appl. Sci. 2017, 7, 625.

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