Can smartphones be used to detect an earthquake? Using a machine learning approach to identify an earthquake event

Alham Fikri Aji, I. Putu Edy Suardiyana Putra, Petrus Mursanto, Setiadi Yazid

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

4 Citations (Scopus)

Abstract

The possibility of using smart phone accelerometer to detect earthquake is investigated in this research. Experiments are designed to learn the pattern of an earthquake signal recorded from smart phone's accelerometer. The signal is processed using N-gram modeling as feature extractor for machine learning. For the classifier, this study use Naïve Bayes, Multi-Layer Perceptron (MLP), and Random Forest. Our result shows that the best classification accuracy is achieved by Random Forest method. Its accuracy reached 93.15%. It can be concluded that smart phones can be used as an earthquake detector.

Original languageEnglish
Title of host publication8th Annual IEEE International Systems Conference, SysCon 2014 - Proceedings
PublisherIEEE Computer Society
Pages72-77
Number of pages6
ISBN (Print)9781479920877
DOIs
Publication statusPublished - 1 Jan 2014
Event8th Annual IEEE International Systems Conference, SysCon 2014 - Ottawa, ON, Canada
Duration: 31 Mar 20143 Apr 2014

Publication series

Name8th Annual IEEE International Systems Conference, SysCon 2014 - Proceedings

Conference

Conference8th Annual IEEE International Systems Conference, SysCon 2014
Country/TerritoryCanada
CityOttawa, ON
Period31/03/143/04/14

Keywords

  • earthquake
  • machine learning
  • n-gram
  • signal processing

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