Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models

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 …

classification cs.ai cs.cv data data-driven earthquake earthquakes face forecasting humans hybrid intensity linear linear regression location natural natural disasters parameters prediction regression risks saving study