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Computational Intelligence Techniques for Predicting Earthquakes

 

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Author: Martínez Álvarez, F.
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
Date: 2011
Published in: Hybrid Artificial Intelligent Systems : 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part II. Lecture Notes in Computer Science, v.6679
ISBN/ISSN: 978-3-642-21221-5
0302-9743
Document type: Chapter of Book
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.
Size: 231.6Kb
Format: PDF

URI: http://hdl.handle.net/11441/42288

DOI: http://dx.doi.org/10.1007/978-3-642-21222-2_35

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