10.5061/DRYAD.9TQ97
Last, Mark
Ben-Gurion University of the Negev
Rabinowitz, Nitzan
Human Monitoring Ltd., Rehovot, Israel
Leonard, Gideon
Israel Atomic Energy Commission
Data from: Predicting the maximum earthquake magnitude from seismic data
in Israel and its neighboring countries
Dryad
dataset
2016
Earthquake prediction
Seismology
time series
Data mining
feature selection
seismicity
2016-12-28T00:00:00Z
2016-12-28T00:00:00Z
en
https://doi.org/10.1371/journal.pone.0146101
213383 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
This paper explores several data mining and time series analysis methods
for predicting the magnitude of the largest seismic event in the next year
based on the previously recorded seismic events in the same region. The
methods are evaluated on a catalog of 9,042 earthquake events, which took
place between 01/01/1983 and 31/12/2010 in the area of Israel and its
neighboring countries. The data was obtained from the Geophysical
Institute of Israel. Each earthquake record in the catalog is associated
with one of 33 seismic regions. The data was cleaned by removing
foreshocks and aftershocks. In our study, we have focused on ten most
active regions, which account for more than 80% of the total number of
earthquakes in the area. The goal is to predict whether the maximum
earthquake magnitude in the following year will exceed the median of
maximum yearly magnitudes in the same region. Since the analyzed catalog
includes only 28 years of complete data, the last five annual records of
each region (referring to the years 2006–2010) are kept for testing while
using the previous annual records for training. The predictive features
are based on the Gutenberg-Richter Ratio as well as on some new seismic
indicators based on the moving averages of the number of earthquakes in
each area. The new predictive features prove to be much more useful than
the indicators traditionally used in the earthquake prediction literature.
The most accurate result (AUC = 0.698) is reached by the Multi-Objective
Info-Fuzzy Network (M-IFN) algorithm, which takes into account the
association between two target variables: the number of earthquakes and
the maximum earthquake magnitude during the same year.
Preprocessed yearly records of seismic eventsTraining data file:
Top10-all-win2-train.csv (136 records related to years 1988-2005). Testing
data file: Top10-all-win2-test.csv (49 records related to years
2006-2010). The data includes only top 10 areas having the highest number
of earthquakes. The list of top 10 areas is shown in Table 1 of the paper.
The format of all files in the folder is compatible with the IN / M-IFN
software.Top10-IFN - Upload.zipInformation Network (IN) and
Multi-Objective Info-Fuzzy Network (M-IFN) SoftwareIN and M-IFN Software
(V. 1.90 .NET Framework 2.0). Developed by Mark Last. Further information
is available in the ReadMe file.IFN-22-Oct-2014.zip
Lebanon
Cyprus
Egypt
Israel
Jordan
Syria