Name
Abstract | ||
|---|---|---|
Nowadays, much effort is being devoted to develop techniques that forecast natural disasters in order to take precautionary measures. In this paper, the extraction of quantitative association rules and regression techniques are used to discover patterns which model the behavior of seismic temporal data to help in earthquakes prediction. Thus, a simple method based on the k-smallest and k-greatest values is introduced for mining rules that attempt at explaining the conditions under which an earthquake may happen. On the other hand patterns are discovered by using a tree-based piecewise linear model. Results from seismic temporal data provided by the Spanish's Geographical Institute are presented and discussed, showing a remarkable performance and the significance of the obtained results. |
Year | Venue | Keywords |
|---|---|---|
2011 | HAIS (2) | earthquakes prediction,hand pattern,natural disaster,geographical institute,seismic temporal data,quantitative association rule,precautionary measure,k-greatest value,mining rule,tree-based piecewise linear model,computational intelligence technique,time series,regression |
Field | DocType | Volume |
Data mining,Computational intelligence,Regression,Computer science,Natural disaster,Temporal database,Association rule learning,Artificial intelligence,Earthquake prediction,Machine learning,Piecewise linear model | Conference | 6679 |
ISSN | Citations | PageRank |
0302-9743 | 10 | 0.93 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Martínez-Álvarez | 1 | 107 | 7.93 |
| A. Troncoso | 2 | 102 | 7.78 |
| A. Morales-Esteban | 3 | 118 | 8.85 |
| J. C. Riquelme | 4 | 239 | 14.01 |