Using Machine Learning Models for Earthquake Magnitude Prediction in California, Japan, and Israel

Deborah Novick, Mark Last

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

2 Scopus citations

Abstract

This study aims at predicting whether an earthquake of magnitude greater than the regional median of maximum yearly magnitudes will occur during the next year. Prediction is performed by training various machine learning algorithms, such as AdaBoost, XGBoost, Random Forest, Logistic Regression, and Info-Fuzzy Network. The models are induced using a combination of seismic indicators used in the earthquake literature as well as various time-series features, such as features based on the moving averages of the number of earthquakes in each area, features that record the number of events above and below the mean in a time period, and features based on lagged values of the mean and median magnitude. Feature selection is performed using a forward search algorithm that chooses the most effective features for prediction. The models are trained and evaluated using earthquake catalog data obtained for California, Japan, and Israel. In addition, models trained on either California or Japan datasets are evaluated using the remaining data. Models trained on Japan data achieve AUC scores up to 0.825; models trained on California data achieve AUC scores up to 0.738; and models trained on Israel data achieve AUC scores up to 0.710.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
EditorsShlomi Dolev, Ehud Gudes, Pascal Paillier
PublisherSpringer Science and Business Media Deutschland GmbH
Pages151-169
Number of pages19
ISBN (Print)9783031346705
DOIs
StatePublished - 1 Jan 2023
Event7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023 - Be'er Sheva, Israel
Duration: 29 Jun 202330 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13914 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
Country/TerritoryIsrael
CityBe'er Sheva
Period29/06/2330/06/23

Keywords

  • Classification models
  • Clustering analysis
  • Earthquake prediction
  • Seismicity indicators

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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