# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 1 Introduction  
Rockfalls are a type of geological hazard occurring in mountainous regions with steep slopes and rock material, categorized under general landslides. [PERSON] [1] classified landslides into falls, topples, slides, spreads, and flows, with rockfalls being a specific type of fall movement involving rock rather than engineering soils. The susceptibility of highways to rockfall hazards is a critical concern due to the potential risks to traffic safety and infrastructure. Despite the importance of this issue, research focusing specifically on rockfall susceptibility along highways is relatively scarce, as most studies address broader regions and various types of slope instability rather than the specific dangers posed by rockfalls on highways. This underscores the need for more targeted studies in this area. Traditionally, rockfall hazards have been analyzed using rating systems. For instance, in mainland China, many highways constructed in the western mountainous region face significant rockfall hazards. To address this, a rockfall rating system based on an Italian method was applied, adjusting weights for rockfall hazard factors and vehicle vulnerability factors to improve accuracy [2]. Additionally, another study in Italy developed a methodology to assess rockfall hazards and associated risks along transportation networks [3]. This approach combines historical recurrence data, frequency-volume statisticsfrom recent rockfall events, and a physically-based, spatially distributed rockfall simulation model. By integrating this information within a Geographic Information System (GIS), the study identifies road sections potentially at risk of rockfalls. Expanding methodological approaches, another study in the Swiss Alps enhances regional rock-mass-failure susceptibility assessment by integrating detailed slope angle analysis of recent Digital Elevation Models (DEMs) with the Slope Angle Distribution procedure [4]. Introducing a normalized cumulative distribution function provides quantitative slope angle weighting, improving susceptibility assessments. Using GIS-based software Flow-R, the method also assesses rockfall runout, enabling the creation of hazard and risk maps. In a different geographic context, research from India examines rockfall issues on highly jointed, near-vertical rock cut slopes, excavated without thorough geotechnical and geological investigations along a riverbank [5]. Field inspections and kinematic analyses identified probable slope failure zones, and the study employed 2D rockfall analysis to estimate energy loss from falling blocks impacting the slope face and the road. Further research in Italy used rockfall occurrence databases and thematic maps to compute a susceptibility map using the Analytical Hierarchy Process (AHP) [6]. The results underscore the significance of morphometric factors in rockfall phenomena. Another study in Iran combined AHP with Weight of Evidence (WoE) and Frequency Ratio (FR) to evaluate rockfall susceptibility mapping [7]. Data from 34 rockfall locations were compiled from various sources, identifying eight factors influencing rockfalls. The results indicate that the WoE method provides better prediction accuracy for rockfall susceptibility mapping compared to the AHP and FR methods. Additionally, a study in Iran analyzed rockfall susceptibility mapping using Artificial Neural Networks (ANN) with 15 factors and data from 57 historical rockfalls [8]. The findings revealed that the northern part of the studied region has a high risk of rockfall failures. Another approach emphasizes the optimization of algorithm hyperparameters for rockfall susceptibility mapping in China [9]. The authors constructed a geological database containing 220 historical rockfalls and 220 non-rockfall cells. Through their analysis using recursive feature elimination, they identified 9 key factors from a list of 23, underscoring the significance of feature selection in improving model performance.