Data-Driven Prediction of Seismic Intensity Distributions Featuring
  Hybrid Classification-Regression Models
Koyu Mizutani Haruki Mitarai Kakeru Miyazaki Soichiro Kumano Toshihiko Yamasaki
Earthquakes are among the most immediate and deadly natural disasters that
humans face. Accurately forecasting the extent of earthquake damage and
assessing potential risks can be instrumental in saving numerous lives. In this
study, we developed linear regression models capable of predicting seismic
intensity distributions based on earthquake parameters: location, depth, and
magnitude. Because it is completely data-driven, it can predict intensity
distributions without geographical information. The dataset comprises seismic
intensity data from earthquakes that occurred in the vicinity of Japan between
1997 and 2020, specifically containing 1,857 instances of earthquakes with a
magnitude of 5.0 or greater, sourced from the Japan Meteorological Agency. We
trained both regression and classification models and combined them to take
advantage of both to create a hybrid model. The proposed model outperformed
commonly used Ground Motion Prediction Equations (GMPEs) in terms of the
correlation coefficient, F1 score, and MCC. Furthermore, the proposed model can
predict even abnormal seismic intensity distributions, a task at conventional
GMPEs often struggle.