**3. Model**

We chose the population density, magnitude, focal depth, epicentral intensity, and time as the input parameters. The criteria and reasons for selecting five input parameters among ten features were as follows:


### *3.1. Data*

We collected 289 destructive earthquakes occurred in the Chinese mainland from 1992 to 2017 in the Earthquake Disasters and Losses Assessment Report in Chinese Mainland (Table A1). The excel table data were pre-processed by openpyxl module in deep learning method, and the data set of 228 earthquake cases were returned without a missing value. Among these data, we selected 180 data as the training set and 38 data as the test set. The remaining ten were used as validation sets. The time was recorded in minutes, and the hours are converted into minutes, which are calculated at 1440 min per day. When the time is x: y, the data will be processed as (60x+y)/144. For example, if the time is 04:32, the change is 0.19. Other parameters need not be specially processed.
