A new earthquake prediction system is presented in this work. This method, based on the
application of artificial neural networks, has been used to predict earthquakes in Chile, one …

Earthquake recurrence models are the basis of seismic hazard analysis and seismic risk
evaluation of physical infrastructure. They are based on statistical analysis of earthquake …

This work explores the use of different seismicity indicators as inputs for artificial neural
networks. The combination of multiple indicators that have already been successfully used …

The use of different seismicity indicators as input for systems to predict earthquakes is
becoming increasingly popular. Nevertheless, the values of these indicators have not been …

Reinforcement and evolutionary algorithms have been applied with a variety of features and
parameters to predict the earthquake in the past. Normal artificial intelligence practices have …

A possibility of applying the density-based clustering algorithm Rough-DBSCAN for
earthquake zoning is considered in the paper. By using density-based clustering for …

The prediction of earthquakes is a task of utmost difficulty that has been widely addressed by
using many different strategies, with no particular good results thus far. Seismic time series …

Analyzing microarray data represents a computational challenge due to the characteristics
of these data. Clustering techniques are widely applied to create groups of genes that …

Seismogenic source zone models, including the delineation and the characterization, still
have a role to play in seismic hazard calculations, particularly in regions with moderate or …

Increasing attention has been paid to the prediction of earthquakes with data mining
techniques during the last decade. Several works have already proposed the use of certain …