## 3 Development of Prediction Model of Ground Subsidence
### Description of Database
#### 3.1.2 Explanatory Valuables  
For the ground motion index, the Japan Meteorological Agency (JMA) seismic intensity [46] at the engineering bedrock (Ia_min_dl) was selected; this can be calculated from the JMA seismic intensity at the surface (Ia) and the amplification of the JMA seismic intensity between the engineering bedrock and ground surface (dl). Furthermore, the maximum elevation (Max_Elev), minimum elevation (Min_Elev), relative relief (Relief), maximum slope angle (Max_Slop_Deg), and minimum slope angle (Min_Slop_Deg) were calculated from the Digital National Land Information [47], and the minimum distances from the center point of the MESH5 to the coastline (Coast_Dist) and the river (River_Dist) were calculated to consider the degree of ground saturation. The average S-wave velocity (AVS30) of the ground to a depth of 30 m and the geomorphologic classification (JCODE) were derived from the dataset developed by [PERSON] and [PERSON] [48]. The height  
Figure 1: Maps of calculated liquefaction ground subsidence for different scenarios related to the Nankai Trough Megathrust Earthquake: (**a**) Fundamental case; (**b**) hypocenter located near the land; (**c**) hypocenter located in the east; (**d**) hypocenter located in the west; (**e**) hypocenter assumed empirically.  
above nearest drainage (HAND) was employed as a parameter to indicate the height from the channel [49; 50].  
To prevent feature competition due to multicollinearity, a correlation analysis was performed, and explanatory variables with high correlations were excluded from the training set. The correlation between each pair of variables was estimated by analyzing Pearson correlation coefficients (PCCs) [51]. If the absolute value of a feature has a PCC > 0.95, it may negatively affect the accuracy of the ML model [51; 52; 53]. Figure 2 shows the PCCs for each explanatory variable.  
Figure 2 shows that there were significant positive correlations between the highest elevation (Max_Elev) and the lowest elevation (Min_Elev), as well as between the minimum slope angle (Min_Slop_Deg) and the relative relief (Relief). Based on the findings, the lowest elevation (Min_Elev) and relative relief (Relief) were removed from the training set after trial and error.  
In addition to the explanatory variables mentioned above, the SPT-N value, which is an indicator of ground strength, was downloaded from the Kunijiban National Ground Information Search site [54]. The obtained data is represented to the orange line in Figure 3. Specifically, SPT-N values were added as explanatory variables for every 1 m depth from the ground surface to a depth of 10 m, as shown by the blue arrow line in Figure 3. However, as shown in Figure 3, the depth at which the SPT-N value was recorded was not always an integer value. Therefore, data interpolation was performed according to the input layer. For the SPT-N map with a depth of 10 m obtained by interpolation, the SPT-N values at the nearest borehole point to the target mesh were assigned as N\({}_{1}\)-N\({}_{10}\). The shortest distance from the center point of MESH5 to the boring point was also added as an explanatory variable (B_Dist). Data were aggregated into MESH5 units.  
Figure 2: The heatmap showing the Pearson correlation coefficients (PCCs) of each explanatory variable in the GIS data correlation.