Time series analysis on earthquakes using EDA and machine learning

Muhammad Fakhrillah Abdul Azis, Fariz Darari, Muhammad Rizqy Septyandy

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

1 Citation (Scopus)

Abstract

An earthquake is a sudden, rapid shaking of the ground caused by the shifting of the Earth's tectonic plates. Earthquakes pose serious threats that cause economic losses and casualties. To mitigate such risks, it is crucial to better understand earthquakes through data-driven analysis. In this paper, we propose an approach to time series analysis over earthquake data, consisting of two steps: exploration and prediction. The exploration step relies on exploratory data analysis (EDA) comprising descriptive statistics and data visualization, whereas the prediction step focuses on how to predict the number of earthquakes for the following years. We perform our time series analysis using various machine learning techniques over a global earthquake dataset from 1965-2016 and report insights as well as lessons learned from the study.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages405-412
Number of pages8
ISBN (Electronic)9781728192796
DOIs
Publication statusPublished - 17 Oct 2020
Event12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 - Virtual, Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020

Conference

Conference12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Country/TerritoryIndonesia
CityVirtual, Depok
Period17/10/2018/10/20

Keywords

  • Earthquake
  • EDA
  • Linear Regression
  • LSTM
  • Machine Learning
  • Prophet
  • Time Series Analysis

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