Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks

This work evaluates artificial neural networks’ accuracy when used to predict earthquakes magnitude in Tokyo. Several seismicity indicators have been retrieved from the literature and used as input for the networks. Some of them have been improved and parameterized in order to extract more valuable knowledge from datasets. The experimental set-up includes predictions for five consecutive datasets referring to year 2015, earthquakes with magnitude larger than 5.0 and for a temporal horizon of seven days. Results have been compared to four well-known machine learning algorithms, reporting very promising results in terms of all quality parameters evaluated. The statistical tests applied conclude that differences between the proposed artificial neural network and the other methods are significant.

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