Quick Search:

The Application of Machine Learning for Predicting Global Seismicity

Shkuratskyy, Viacheslav, Usman, Aminu Bello ORCID: https://orcid.org/0000-0002-4973-3585 and O'Dea, Mike ORCID: https://orcid.org/0000-0003-2112-0194 (2023) The Application of Machine Learning for Predicting Global Seismicity. In: Artificial Intelligence Methods and Applications in Computer Engineering. 1 ed. Engineering Science Reference, 2023, pp. 1-22 (In Press)

[img] Text (The Application of Machine Learning for Predicting Global Seismicity)
The Application of Machine Learning for Predicting Global Seismicity.docx - Accepted Version
Restricted to Repository staff only

Related URLs:

Abstract

An earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as movement of tectonic plates, volcanic eruptions, rainfall, and tidal stress all play an important part in earthquakes. Earthquakes can also be caused by human beings, such as mining, dams, nuclear bomb testing, etc. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, on the Sun’s surface and the Earth’s surface, 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.

Item Type: Book Section
Status: In Press
Subjects: Q Science > Q Science (General) > Q325 Machine learning
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/7102

University Staff: Request a correction | RaY Editors: Update this record