Machine learning for earthquake prediction
Earthquake prediction for the region of Sendai, Japan was carried out in this study by using 7 seismic features as inputs to an artificial neural network. The seismic indicators are selected based on well-known seismological and geophysical facts and are able to represent the seismic state of a sp...
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Nanyang Technological University
2021
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sg-ntu-dr.10356-1485302023-02-28T23:17:08Z Machine learning for earthquake prediction Ang, Grace Li Ling Fedor Duzhin School of Physical and Mathematical Sciences FDuzhin@ntu.edu.sg Science::Geology::Volcanoes and earthquakes Science::Mathematics::Applied mathematics::Simulation and modeling Earthquake prediction for the region of Sendai, Japan was carried out in this study by using 7 seismic features as inputs to an artificial neural network. The seismic indicators are selected based on well-known seismological and geophysical facts and are able to represent the seismic state of a specific region. Prediction results were evaluated using 5 measures: Sn, Sp, P1, P0 and Accuracy. Overall, the prediction accuracy is not satisfactory, suggesting the use of a deeper learning neural network. Bachelor of Science in Mathematical Sciences and Economics 2021-05-04T05:49:27Z 2021-05-04T05:49:27Z 2021 Final Year Project (FYP) Ang, G. L. L. (2021). Machine learning for earthquake prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148530 https://hdl.handle.net/10356/148530 en application/pdf Nanyang Technological University |
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Singapore Singapore |
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Science::Geology::Volcanoes and earthquakes Science::Mathematics::Applied mathematics::Simulation and modeling |
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Science::Geology::Volcanoes and earthquakes Science::Mathematics::Applied mathematics::Simulation and modeling Ang, Grace Li Ling Machine learning for earthquake prediction |
| description |
Earthquake prediction for the region of Sendai, Japan was carried out in this study by using 7
seismic features as inputs to an artificial neural network. The seismic indicators are selected based
on well-known seismological and geophysical facts and are able to represent the seismic state of a
specific region. Prediction results were evaluated using 5 measures: Sn, Sp, P1, P0 and Accuracy.
Overall, the prediction accuracy is not satisfactory, suggesting the use of a deeper learning neural
network. |
| author2 |
Fedor Duzhin |
| author_facet |
Fedor Duzhin Ang, Grace Li Ling |
| format |
Final Year Project |
| author |
Ang, Grace Li Ling |
| author_sort |
Ang, Grace Li Ling |
| title |
Machine learning for earthquake prediction |
| title_short |
Machine learning for earthquake prediction |
| title_full |
Machine learning for earthquake prediction |
| title_fullStr |
Machine learning for earthquake prediction |
| title_full_unstemmed |
Machine learning for earthquake prediction |
| title_sort |
machine learning for earthquake prediction |
| publisher |
Nanyang Technological University |
| publishDate |
2021 |
| url |
https://hdl.handle.net/10356/148530 |
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1759856949929705472 |