Name
Abstract | ||
|---|---|---|
We construct a classification model, that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from now. A common approach is to use expert-generated features like Region-Time-Length (RTL) features as an input to the model. The proposed approach aggregates of multiple generated RTL features to take into account effects at various scales and to improve the quality of a machine learning model. For our data on Japan earthquakes 1992-2005 and predictions at locations given in this database, the best model provides precision as high as 0.95 and recall as high as 0.98. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-24289-3_41 | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT I: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 1-4, 2019, PROCEEDINGS, PT I |
Keywords | DocType | Volume |
Machine learning, RTL features, Earthquakes prediction | Journal | 11619 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Proskura | 1 | 0 | 0.34 |
| A. Zaytsev | 2 | 0 | 1.01 |
| I. Braslavsky | 3 | 0 | 0.34 |
| E. Egorov | 4 | 0 | 0.34 |
| Evgeny Burnaev | 5 | 119 | 42.78 |