The Application of Machine Learning for Predicting Global Seismicity

View Sample PDF

Author(s): Viacheslav Shkuratskyy (York St. John University, UK), Aminu Bello Usman (York St. John University, UK)and Michael S. O'Dea (York St. John University, UK)
Copyright: 2023
Pages: 31
Source title: Handbook of Research on AI Methods and Applications in Computer Engineering
Source Author(s)/Editor(s): Sanaa Kaddoura (Zayed University, UAE)
DOI: 10.4018/978-1-6684-6937-8.ch011

Keywords: Artificial Intelligence / Computer Engineering / Computer Science & IT / Engineering Science Reference

Purchase

View The Application of Machine Learning for Predicting Global Seismicity on the publisher's website for pricing and purchasing information.

Abstract

An earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as rainfall or volcanic eruptions disrupt data. Earthquakes can also be caused by human beings, such as mining or dams. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, separated by a huge distance. However, scientists have been trying to figure out if there are any links between these two seemingly unrelated occurrences since the 19th century. In this chapter, the authors explored the methods of how machine learning algorithms including k-nearest neighbour, support vector regression, random forest regression, and long short-term memory neural networks can be applied to predict earthquakes and to understand if there is a relationship between solar activity and earthquakes. The authors investigated three types of solar activity: sunspots number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth.

The IRMA Community

Research IRM

The Application of Machine Learning for Predicting Global Seismicity

Purchase

Abstract

Related Content

IRMA Sponsors