Large earthquake magnitude prediction in Chile with imbalanced classifiers and ensemble learning

Abstract

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.