Article citationsMore>>
Bhandarkar, T., Satish, N., Sridhar, S., Sivakumar, R., & Ghosh, S. (2019). Earthquake Trend Prediction Using Long Short-Term Memory RNN. International Journal of Electrical & Computer Engineering, 9, 1304-1312.
https://doi.org/10.11591/ijece.v9i2.pp1304-1312
has been cited by the following article:
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TITLE:
Earthquake Prediction Software on Global Scale
AUTHORS:
Haruhiro Shiraishi
KEYWORDS:
Dissociation, Earthquake, Global Scale, Machine Learning
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.10 No.3,
March
7,
2022
ABSTRACT: Researchers developed an earthquake prediction software and evaluated its
performance. This earthquake prediction software is suitable for short-term earthquake
prediction. This approach relies on the deep involvement of water vapors that
occur just before an earthquake caused by a decrease in pressure and an
increase in temperature, leading to a 70.5% prediction accuracy within a month
in Japan. In addition, we have tried to develop a new practical method to warn
earthquakes for not only Japan but also global scale. In other words, this
paper is dedicated to improving the short-term earthquake prediction software
from Japan to global scale. In global scale, the prediction rate improved to
80.8%.