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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:

• 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%.