## 1 Introduction
Logistic regression can analyze the relative importance of each indicator in predicting the probability of rockfall occurrence, allowing for an objective, accurate, and effective rockfall risk assessment [35]. This study introduces the application of the information quantity method, logistic regression, and fuzzy hierarchical analysis for the first time to evaluate rockfall risk. This approach effectively elucidates the impact of specific factors and their corresponding ranges on rockfall incidents, minimizes the influence of human factors, and enables precise and rapid risk assessment over large areas of complex terrain. Numerous research studies have utilized rockfall susceptibility to evaluate the risk associated with it [36; 37]. The assessment of highway rockfall risk typically involves three main components: highway rockfall susceptibility, highway rainfall hazard, and highway rockfall vulnerability. Rockfall susceptibility pertains to the probability of rockfall events occurring and is influenced by intrinsic factors such as slope. Consequently, evaluating these elements is crucial for assessing rockfall susceptibility. This study proposes a new method, which adopts logistic regression and a fuzzy analytic hierarchy process to evaluate susceptibility, rainfall hazard, and vulnerability, respectively, and uses the risk matrix to assess the risk of rockfall. This method can assess the risk of rockfalls quickly and accurately, and effectively reduce the risk of rockfall disasters. The normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI) were selected as the evaluation indexes of rockfall susceptibility. Methods like the information content method and logistic regression are commonly used to determine highway rockfall susceptibility. While existing research has explored various individual methods for evaluating rockfall susceptibility, there has been no attempt to combine these methods. Integrating these approaches could enhance the analysis of each factor's impact and its variability on rockfall occurrences, thereby reducing the reliance on extensive datasets and mitigating potential biases introduced by human subjectivity. The highway rainfall hazard is determined by factors like average annual rainfall and daily maximum rainfall. On the other hand, highway rockfall vulnerability is influenced by variables such as GDP, population, and land use. By combining highway rockfall vulnerability, highway rainfall hazard, and highway rockfall susceptibility, a risk matrix can be generated to determine the overall highway rockfall risk. The research focused on assessing the highway rockfall risk in Guizhou Province. The accuracy of the risk assessment was verified using the receiver operating characteristic (ROC) curve. This study aims to provide recommendations for the prevention and management of rockfall disasters on highways in Guizhou Province. The main structure of this article is as follows: Section 2 illustrates the basic situation of Guizhou Province. Section 3 illustrates the risk assessment methods and indicators. Section 4 illustrates the rockfall risk assessment results in Guizhou Province. In Section 5, the accuracy of the model and the key indicators affecting rockfalls are discussed. Conclusions are drawn in Section 6.