## 5 Discussion  
The accuracy of a model usually needs to be verified after the model has been built to calculate the risk results. The receiver operating characteristic (ROC) curve provides a simple and intuitive validation of model accuracy by analyzing the sensitivity and specificity of continuous variables. Moreover, the ROC curve avoids the interference of excessive human factors in the accuracy verification of the results, with good objectivity. Nowadays, ROC curves are widely used to verify the accuracy of geological risk assessments. In this research, 250 random disaster points with an equal number of non-disaster points at intervals of more than 1 km apart were selected, and their probability values were extracted. The data was then imported into SPSS Statistics 26.0 (IBM Corp., Armonk, NY, USA) to verify the accuracy of the risk assessment. The ROC curve for the highway rockfall risk assessment of Guizhou Province is shown in Figure 17. The precision-recall (PR) curve was used to better evaluate the model's performance under class imbalance. The average precision (AP) was 0.765, indicating a good capability to identify very high-risk zones despite their relatively low proportion. The precision-recall curve for the highway rockfall risk assessment of Guizhou Province is shown in Figure 18. The area under the ROC curve in Guizhou Province is 0.827, which indicates that the result of the highway rockfall risk assessment in this area is accurate and reliable. Figure 16: Highway rockfall risk mileage by district in Guizhou Province. In order to further validate the accuracy of the model, this paper compares it to the report on the trend prediction of sudden geologic disasters in Guizhou Province issued by the Guizhou Provincial Government in 2023. In this report, the geologic disasters that occurred in 2022 in each district of Guizhou Province were counted, and the specific statistical results are shown in Table 8. According to Table 8, LPS had the highest number of rockfall accidents, which caused much higher economic losses than experienced by the other districts. Although the number of rockfalls in BJ was not as high, the economic loss caused by it was second only to that of LPS, which may be due to the fact that BJ and LPS are not as  
have the largest proportion of very high-risk and high-risk areas, and their risk level is much higher than that of the other districts. Moreover, QDN has a large proportion of low- and medium-risk areas, so its economic losses were small. Thus, the accuracy of the evaluation model developed in this paper is further demonstrated. Rockfall is one of the most common natural disasters. Once it occurs, it will cause a huge hazard. In recent years, the issue of how to assess the risk of rockfalls and manage and prevent them has become a hot topic. A number of studies have selected and analyzed some of the elements that influence the occurrence of rockfalls to obtain the results of rockfall risk. In this study, the highway rockfall risk is considered to be composed of three components: highway rockfall susceptibility, highway rainfall hazard, and highway rockfall vulnerability. Highway rockfall susceptibility is the probability that internal factors influence the occurrence of rockfalls; highway rainfall hazard is the size of the hazard posed by the occurrence of rockfall due to rainfall; and highway rockfall vulnerability is the size of the hazard brought about by the occurrence of rockfalls. With the further development of the research, many methods for rockfall risk assessment have emerged, which are mainly categorized into two types: qualitative methods and quantitative methods. The qualitative methods include the expert empirical method, fuzzy comprehensive evaluation method, and spatial principal component analysis method. Quantitative analysis methods include the AHP method, information content method, frequency ratio method, and machine learning method.