Chapter of Book
Computational Intelligence Techniques for Predicting Earthquakes
Author/s | Martínez Álvarez, Francisco
Troncoso Lora, Alicia Morales Esteban, Antonio Riquelme Santos, José Cristóbal |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Estructuras de Edificación e Ingeniería del Terreno |
Publication Date | 2011 |
Deposit Date | 2016-06-15 |
Published in |
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ISBN/ISSN | 978-3-642-21221-5 0302-9743 |
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 ... 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. |
Funding agencies | Ministerio de Ciencia y Tecnología (MCYT). España Junta de Andalucía |
Project ID. | TIN2007-68084-C-02
P07-TIC-02611 |
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