Earthquake fault systems interact over a broad spectrum of spatial and temporal scales and,
in recent years, studies of the regional seismicity in a variety of regions have produced a …

Earthquake prediction has been a challenging research area, where a future occurrence of
the devastating catastrophe is predicted. In this work, sixty seismic features are computed …

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 …

Recent advances in data collection technology, accompanied by the ever-rising volume and
velocity of streaming data, underscore the vital need for time series analytics. In this regard …

For several years, parts of the content of this textbook have been used in undergraduate
courses in the Department of Mathematics and in the Faculty of Economics at the University …

Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …

Earthquake magnitude prediction is a challenging problem that has been widely studied
during the last decades. Statistical, geophysical and machine learning approaches can be …

In this paper, a deep learning-based method for earthquake prediction is proposed. Large-
magnitude earthquakes and tsunamis triggered by earthquakes can kill thousands of people …

This work evaluates artificial neural networks' accuracy when used to predict earthquakes
magnitude in Tokyo. Several seismicity indicators have been retrieved from the literature …

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 …