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:

• 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%.