Improving earthquake prediction with principal component analysis: Application to Chile

Abstract

Increasing attention has been paid to the prediction of earthquakes with data mining techniques during the last decade. Several works have already proposed the use of certain features serving as inputs for supervised classifiers. However, they have been successfully used without any further transformation so far. In this work, the use of principal component analysis to reduce data dimensionality and generate new datasets is proposed. In particular, this step is inserted in a successfully already used methodology to predict earthquakes. Santiago and Pichilemu, two of the cities mostly threatened by large earthquakes occurrence in Chile, are studied. Several well-known classifiers combined with principal component analysis have been used. Noticeable improvement in the results is reported.