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Erschienen in:
Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Improving Earthquake Prediction with Principal Component Analysis: Application to Chile

verfasst von : Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Antonio Morales-Esteban, Jorge Reyes, Alicia Troncoso

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Neuro-Fuzzy Analysis of Atmospheric Pollution
Nächstes Kapitel Detecting Anomalies in Embedded Computing Systems via a Novel HMM-Based Machine Learning Approach
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Metadaten
Titel
Improving Earthquake Prediction with Principal Component Analysis: Application to Chile
verfasst von
Gualberto Asencio-Cortés
Francisco Martínez-Álvarez
Antonio Morales-Esteban
Jorge Reyes
Alicia Troncoso
Copyright-Jahr
2015
Buch
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-19643-5

Electronic ISBN: 978-3-319-19644-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-19644-2_33