Article citationsMore>>
Tan, X., Ma, Y., Jiao, Z., Su, L., Ma, A., Hou, J., & Yan, L. (2014). Modeling Pre-Earthquake Cloud Shape from Remote-Sensing Images. In Q. H. Weng, P. Gamba, G. Xian, G. X. Wang, & J. J. Zhu (Eds.), 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA) (pp. 470-474). IEEE.
https://doi.org/10.1109/EORSA.2014.6927935
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%.
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