# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 3 Results
### Feature Importance Visualization  
Figure 5 illustrates the variable importance derived from the Random Forest model. The 28 predictors are categorized into five groups: geographical, topographical, environmental, geological, and socioeconomic variables, each labeled with different colors for clarity. The figure shows that the most influential factor is slope_deg, a topographical variable, indicating its significant impact on the model's predictions. Following closely are dist_geo_sens_area, a geographical variable, and dist_urban, another geographical variable, highlighting the crucial role of topographical and geographical features. Other notable variables include curvature, spi, twd97_y, twd97_x, and twi, which also fall within the geographical and topographical categories. Environmental variables are ranked next, indicating that they are of lesser importance compared to topographical and geographical variables. The socioeconomic variable pop_density and the two geological variables rank near the bottom of the figure, suggesting their relatively minor impact on rockfall occurrence predictions. Overall, this figure highlights the model's ability to integrate a diverse range of variables across different domains, reflecting the complex interplay of geographical, topographical, environmental, geological, and socioeconomic factors in the analysis. The mean SHAP values shown in Figure 6 represent the average impact of various features on the output of the machine learning model. The features are ranked by importance, with slope_deg and dist_geo_sens_area exhibiting the highest mean SHAP values of 0.0422 and 0.0368, respectively, signifying their significant influence on the model's predictions. Other notable features, such as curvature, spi, avg_yr_rainy_days, and twi, also contribute meaningfully, albeit to a lesser extent. The remaining features display lower mean SHAP values, reflecting their gradually diminishing impact on the model's  
Figure 5: Variable importance derived from the Random Forest model, highlighting the significant predictors across geographical, topographical, environmental, geological, and socioeconomic categories. predictions. This plot provides a clear hierarchy of feature importance, enhancing our understanding of which variables most strongly influence the model's outcomes. The comparison between the Random Forest feature importance and the SHAP values demonstrates a strong alignment in identifying key predictors. Both methods concur on the top features, with slope_deg and dist_geo_sens_area consistently ranked as the two most important variables. Additionally, the top four features in the SHAP values are among the top five features identified by the Random Forest. However, there are some minor differences; for instance, SHAP values assign greater importance to avg_yr_rainy_days compared to its ranking in the Random Forest results, whereas Random Forest ranks twd97_y and twd97_x higher than the SHAP values do. Despite these differences, both methods emphasize the critical significance of the top features, particularly those related to geographical and topographical variables. SHAP values enhance interpretability by quantifying the average contribution of each feature, complementing the Random Forest's ranking. This similarity between the two methods supports confidence in the model's identification of key predictors, providing a robust understanding of the features driving the model's predictions. The SHAP values illustrated in Figure 7 demonstrate the impact of each feature on the model's output, providing a detailed understanding of feature importance and interaction. Each dot represents a SHAP value for a specific feature and instance. The position on the x-axis shows the feature's impact on the model's output, with values ranging from negative to positive. The color of the dots indicates the feature value, with red representing high values and blue representing low values. Figure 6: Mean SHAP values indicating the average impact of each feature on model predictions.