## 5 Discussion
The present methodology for evaluating rockfall risk faces the following challenges: Firstly, the weighted combination of various evaluation indicators is greatly influenced by human subjectivity and cannot objectively reflect the impact of different indicators on geological disasters (as is the case with the AHP method and expert empirical method). Furthermore, a large amount of accurate data is required for modeling and analysis, and insufficient and inaccurate data can have a significant impact on the accuracy of the overall assessment model (as is the case with the machine learning method). In comparison, the information content method analyzes the area of the interval of each rockfall hazard impact indicator and the number of rockfall hazards to get the information content of the probability of the occurrence of a rockfall accident due to each rockfall impact indicator. In this research, we first used the information content method to assign information content to each indicator zone, then used logistic regression to assign different weights to different indicators, and finally combined them to obtain the results of highway rockfall susceptibility. This research method fully considers the objective influence of evaluation indexes on rockfall disasters and effectively avoids the influence of human subjectivity, and its evaluation results are more scientific and reasonable. The impact of each rockfall susceptibility indicator zone could be determined for the entire study area based on the information content values, and a combination of the highest risk zones for highway rockfall susceptibility could be identified. The dominant \begin{table}
\begin{tabular}{c c c} \hline \hline
**District** & **Rockfall Number** & **Economic Loss (Thousands)** \\ \hline LPS & 5 & 29,000 \\ BJ & 2 & 21,100 \\ QXN & 3 & 4902 \\ TR & 2 & 2600 \\ GR & 2 & 1700 \\ QN & 1 & 1100 \\ AS & 1 & 900 \\ ZY & 3 & 720 \\ QDN & 2 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistics of geologic disasters in each district of Guizhou Province in 2022.  
influencing factors that can easily induce highway rockfall hazards in Guizhou Province include elevation \(>\) 1500 m, slope of 25-40\({}^{\circ}\), NDVI \(<\) 0.2, aspect of south, distance from fault \(<\)1.5 km, relief amplitude \(>\) 450 m, lithology being 1 (very soft rock), and RWI of 0.6-0.8. The information content can only reflect the effect on the probability of rockfall occurrence within a single indicator of highway rockfall susceptibility, whereas logistic regression can analyze the ranked size of the effect of each indicator on the probability of rockfall occurrence. The results of the logistic regression ranking of rockfall susceptibility are shown in Figure 19. The RWI is the most important factor influencing the probability of rockfall. The distance from the fault is the most negatively correlated indicator of the probability of rockfall. The greater the distance from the fault, the lower the probability of rockfall. According to the above conditions, the probability of rockfall can be effectively determined, and the probability of rockfall accidents is the highest when there is an area where most of the evaluation indexes belong to the zone of the main impact indexes. The relevant departments can enhance the protection and management of rockfalls in the area to minimize the hazards caused by rockfall accidents. In this research, highway rockfall susceptibility, highway rainfall hazards, and highway rockfall vulnerability were considered when evaluating the highway rockfall risk results for the whole research. This can provide scientific and reasonable protection management advice to the relevant authorities for decision-making and management of highway rockfall risks. For areas with high rockfall risks, such as Bijie, regular monitoring and the establishment of rockfall protection nets can be implemented to reduce the damage caused by rockfalls.