Usage of Multiple RTL Features for Earthquakes Prediction

P. Proskura, A. Zaytsev, I. Braslavsky, E. Egorov, E. Burnaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

We construct a classification model, that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30–180 days from now. A common approach is to use expert-generated features like Region-Time-Length (RTL) features as an input to the model. The proposed approach aggregates of multiple generated RTL features to take into account effects at various scales and to improve the quality of a machine learning model. For our data on Japan earthquakes 1992–2005 and predictions at locations given in this database, the best model provides precision as high as 0.95 and recall as high as 0.98.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings
EditorsSanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Eufemia Tarantino, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan
PublisherSpringer Verlag
Pages556-565
Number of pages10
ISBN (Print)9783030242886
DOIs
Publication statusPublished - 2019
Event19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation
Duration: 1 Jul 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11619 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Computational Science and Its Applications, ICCSA 2019
Country/TerritoryRussian Federation
CitySaint Petersburg
Period1/07/194/07/19

Keywords

  • Earthquakes prediction
  • Machine learning
  • RTL features

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