Earthquake magnitude prediction based on artificial neural networks: A survey

Authors

  • Emilio Florido Pablo de Olavide University
  • José L. Aznarte Department of Articial Intelligence, Universidad Nacional de Educación a Distancia-UNED
  • Antonio Morales-Esteban Department of Building Structures and Geotechnical Engineering, University of Seville
  • Francisco Martínez-Álvarez Division of Computer Science, Pablo de Olavide University

Abstract

The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate entire cities in just a few seconds, causing numerous casualties and huge economic loss. Great effort has been directed towards being able to predict these natural disasters, and taking precautionary measures. However, simultaneously predicting when, where and the magnitude of the next earthquake, within a limited region and time, seems an almost impossible task. Techniques from the field of data mining are providing new and important information to researchers. This article reviews the use of artificial neural networks for earthquake prediction in response to the increasing amount of recently published works and presenting claims of being effective. Based on an analysis and discussion of recent results, data mining practitioners are encouraged to apply their own techniques in this emerging field of research.

Author Biographies

Emilio Florido, Pablo de Olavide University

Division of Computer Science, Pablo de Olavide University

José L. Aznarte, Department of Articial Intelligence, Universidad Nacional de Educación a Distancia-UNED

Department of Artificial Intelligence

Antonio Morales-Esteban, Department of Building Structures and Geotechnical Engineering, University of Seville

Department of Building Structures and Geotechnical Engineering

Downloads

Published

2016-12-30

Issue

Volume 7 (2016), Number 2

Section

CRORR Journal Regular Issue

License

- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal

- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).