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Toward Predicting Global Seismicity of the Earth using Machine Learning Techniques and Solar Activity Data

Shkuratskyy, Viacheslav (2022) Toward Predicting Global Seismicity of the Earth using Machine Learning Techniques and Solar Activity Data. Masters thesis, York St John University.

Text (MSc by Research thesis)
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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 study, four machine learning algorithms k-nearest neighbour, support vector regression, random forest regression, and Long Short-Term Memory network were applied to understand if there is a relationship between solar activity and earthquakes. The study employed three types of solar activity: sunspot number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth.

The study's findings imply that the Long Short-Term Memory network model predicts earthquakes more accurately than other models. There's a chance that earthquakes are influenced by solar activity. Earthquakes with a magnitude less than 5.5 are more linked to solar activity than earthquakes with a magnitude equal to or higher than 5.5. Solar activity has a bigger impact on earthquakes of lower depths.

Item Type: Thesis (Masters)
Status: Published
Subjects: T Technology > T Technology (General)
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8066

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