# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 2 Materials and Methods
### Independent Variables for Rockfall Analysis  
To use machine learning for analyzing the rockfall hazard of Taiwan's highway system, it is essential to collect independent variables related to rockfall occurrences. These variables, known as features or predictors, improve the accuracy of predictions. Gathering a diverse set of predictors helps in capturing the complex interactions between various factors influencing rockfall events, thereby enhancing the model's predictive power. In this study, we drew on our extensive experience with machine learning on soil erosion depths [15], cover management factors [16], and global vegetation growth [17] to compile a comprehensive list of 28 predictive variables. These variables are categorized into five groups: geographical, topographical, environmental, geological, and socioeconomic, as shown in Table 2. This categorization allows for a structured approach in analyzing the impact of different types of variables on rockfall occurrences. The DEM and population density data in Table 2 were sourced from the Ministry of the Interior. Slope, aspect, curvature, topographic wetness index, stream power index, and terrain ruggedness index were calculated from the DEM. The river network data were obtained from the Water Resources Agency. Fault lines, geologically sensitive areas, epoch, and stratum data were provided by the Geological Survey and Mining Management \begin{table}
\begin{tabular}{l l l} \hline \hline
**No.** & **Variable** & **Description** \\ \hline \multicolumn{3}{l}{Geographical Variables} \\
1 & twd97\_y & The northing coordinate in the TWD97 coordinate system (also known as the 1997 Taiwan Datum), which is used for mapping and surveying in Taiwan. \\
2 & twd97\_x & The easting coordinate in the TWD97 coordinate system. \\
3 & dist\_river & Distance to the nearest river (meters). \\
4 & dist\_fault & Distance to the nearest fault line (meters). \\
5 & dist\_urban & Distance to the urban boundary (meters). \\
6 & dist\_geo\_sens\_area & Distance to the geologically sensitive area boundary (meters). \\
7 & dist\_debris\_flow\_stream & Distance to the potential debris flow stream (meters). \\
8 & dist\_debris\_flow\_watershed & Distance to the potential debris flow stream watershed boundary (meters). \\
9 & dist\_debris\_flow\_influence & Distance to the potential debris flow stream influence area boundary (meters). \\ \hline \multicolumn{3}{l}{Topographical Variables} \\
10 & elev & Elevation from 20 m resolution DEM (meters). \\
11 & slope\_deg & Slope measured in degrees. \\
12 & slope\_asp & Slope aspect. \\
13 & curvature & Slope curvature. \\
14 & twi & Topographic Wetness Index. \\
15 & spi & Stream Power Index. \\
16 & tri & Terrain Ruggedness Index. \\
17 & river\_density & River density (km/km\({}^{2}\)). \\ \hline \multicolumn{3}{l}{Environmental Variables} \\
18 & avg\_yr\_rainfall & Average yearly rainfall from 2020 to 2023 (mm/year). \\
19 & avg\_yr\_rainy\_days & Average number of rainy days per year from 2020 to 2023 (days). \\
20 & avg\_month\_temp & Average monthly temperature from 2020 to 2023 (\({}^{\circ}\)C). \\
21 & avg\_min\_temp & Average minimum temperature per year from 2020 to 2023 (\({}^{\circ}\)C). \\
22 & avg\_max\_temp & Average maximum temperature per year from 2020 to 2023 (\({}^{\circ}\)C). \\
23 & avg\_month\_wind\_speed & Average monthly wind speed from 2020 to 2023 (m/s). \\
24 & avg\_month\_humidity & Average monthly humidity from 2020 to 2023 (\%). \\
25 & ndvi & Normalized Difference Vegetation Index (NDVI). \\ \hline \multicolumn{3}{l}{Geological Variables} \\
26 & epoch & Geological time period. \\
27 & stratum\_abbr & Stratum abbreviation. \\ \hline \multicolumn{3}{l}{Socioeconomic Variables} \\
28 & pop\_density & Population density (persons/ha). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Independent variables for analyzing rockfall occurrences. Agency. Urban area data were sourced from the Global Urban Boundaries dataset [18].