Earthquake magnitude prediction based on artificial neural networks: A survey

  • 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
Published
2016-12-30
Section
CRORR Journal Regular Issue