University of Twente Student Theses
Analysis and Prediction of Earthquakes using different Machine Learning techniques
Mondol, Manaswi (2021) Analysis and Prediction of Earthquakes using different Machine Learning techniques.
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| Abstract: | A reliable and accurate method for earthquake prediction has the potential to save countless human lives. With that objective in mind, this paper looks into various methods to predict the magnitude and depth of earthquakes. In this paper, real-world earthquake data is analysed to identify patterns and gain insight into this natural calamity. This data is then used to train four machine learning models namely Random forest, linear regression, polynomial regression, and Long Short Term Memory for predicting the magnitude and depth of earthquakes. The performances are compared to find the most effective model. It is very difficult to accurately predict the magnitude of earthquakes however, in this paper it can be seen that polynomial regression shows the best overall results. Also, Random forests are incredibly effective in predicting the depth of an earthquake. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/87313 |
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