Name
Abstract | ||
|---|---|---|
Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which model the behavior of seismic temporal data and can help to predict medium-large earthquakes. First, earthquakes are classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are discovered when medium-large earthquakes happen. Results from the Spanish seismic temporal data provided by the Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable performance and the significance of the obtained results. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2010.05.050 | Expert Syst. Appl. |
Keywords | Field | DocType |
earthquakes forecasting,great effort,spanish geographical institute,seismic time series,pattern recognition,numerous death,seismic temporal data,time series,different group,spanish seismic temporal data,clustering technique,medium-large earthquake,clustering,economical loss,non-parametric statistical test,temporal data,non parametric statistics,natural disaster | Data mining,Computer science,A priori and a posteriori,Natural disaster,Temporal database,Artificial intelligence,Cluster analysis,Machine learning,Statistical hypothesis testing | Journal |
Volume | Issue | ISSN |
37 | 12 | Expert Systems With Applications |
Citations | PageRank | References |
20 | 1.31 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Morales-Esteban | 1 | 118 | 8.85 |
| F. Martínez-Álvarez | 2 | 107 | 7.93 |
| A. Troncoso | 3 | 102 | 7.78 |
| J. L. Justo | 4 | 20 | 1.31 |
| C. Rubio-Escudero | 5 | 36 | 4.12 |