5,236 research outputs found
Computational Intelligence Techniques for Predicting Earthquakes
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.Ministerio de Ciencia y tecnología TIN2007-68084-C-02Junta de Andalucía P07-TIC-0261
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
Earthquakes are among the most immediate and deadly natural disasters that
humans face. Accurately forecasting the extent of earthquake damage and
assessing potential risks can be instrumental in saving numerous lives. In this
study, we developed linear regression models capable of predicting seismic
intensity distributions based on earthquake parameters: location, depth, and
magnitude. Because it is completely data-driven, it can predict intensity
distributions without geographical information. The dataset comprises seismic
intensity data from earthquakes that occurred in the vicinity of Japan between
1997 and 2020, specifically containing 1,857 instances of earthquakes with a
magnitude of 5.0 or greater, sourced from the Japan Meteorological Agency. We
trained both regression and classification models and combined them to take
advantage of both to create a hybrid model. The proposed model outperformed
commonly used Ground Motion Prediction Equations (GMPEs) in terms of the
correlation coefficient, F1 score, and MCC. Furthermore, the proposed model can
predict even abnormal seismic intensity distributions, a task at conventional
GMPEs often struggle
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