Name
Title | ||
|---|---|---|
Using principal component analysis to improve earthquake magnitude prediction in Japan. |
Abstract | ||
|---|---|---|
Increasing attention has been paid to the prediction of earthquakes with data mining techniques during the last decade. Several works have already proposed the use of certain features serving as inputs for supervised classifiers. However, they have been successfully used without any further transformation so far. In this work, the use of principal component analysis (PCA) to reduce data dimensionality and generate new datasets is proposed. In particular, this step is inserted in a successfully already used methodology to predict earthquakes. Tokyo, one of the cities mostly threatened by large earthquakes occurrence in Japan, is studied. Several well-known classifiers combined with PCA have been used. Noticeable improvement in the results is reported. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/jigpal/jzx049 | LOGIC JOURNAL OF THE IGPL |
Keywords | Field | DocType |
Earthquake prediction,principal component analysis,time series,data mining | Seismology,Magnitude (mathematics),Geology,Principal component analysis | Journal |
Volume | Issue | ISSN |
25 | SP6 | 1367-0751 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gualberto Asencio-Cortés | 1 | 4 | 1.43 |
| Francisco Martínez-Álvarez | 2 | 155 | 23.98 |
| A. Morales-Esteban | 3 | 118 | 8.85 |
| Jorge Reyes | 4 | 1 | 0.69 |
| Alicia Troncoso | 5 | 153 | 20.88 |