# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 5 Conclusions  
This study successfully applied machine learning techniques, particularly the Random Forest model, to predict rockfall occurrences near highways. The analysis used a comprehensive dataset comprising 182 rockfall events from April 2019 to February 2024 (4 years and 11 months), and an equal number of non-rockfall instances, incorporating 28 distinct variables. The performance metrics of the Random Forest model demonstrated robust predictive capabilities, with high accuracy, precision, recall, and F1 scores, alongside a substantial Kappa statistic and an impressive AUC value of 0.9317. These results affirm the model's reliability and effectiveness in classifying rockfall occurrences. The comparison between the Random Forest model's variable importance and the SHAP value analysis provided valuable insights into the contributions of various features. Both methods consistently identified slope_deg and dist_geo_sens_area as highly influential in predicting rockfall events. This study also highlights the use of internet news-based data collection and analysis. Internet news not only helps collect information from various sources that might otherwise go unnoticed, but also systematically gathers this information and automatically notifies researchers. Our sustained work over 4 years and 11 months has proven the efficacy of this process. Consequently, this has resulted in the first rockfall hazard susceptibility analysis of Taiwan's entire highway system, rather than just specific roads or sections, a comprehensive scope that is unprecedented in the literature. Overall, the findings underscore the critical importance of incorporating detailed spatial data and leveraging advanced machine learning techniques to enhance predictive accuracy. The complementary use of Random Forest and SHAP analyses provides a robust framework for understanding and improving predictive models, ultimately contributing to safer and more reliable road infrastructure management in rockfall-prone regions. Future research should focus on integrating additional detailed spatial and environmental data to further enhance the predictive capabilities and practical applications of these models. Moreover, efforts should be made to address the biases in data collection and the limitations of severity assessment by exploring alternative data sources or developing new methods to quantify the impact of rockfalls more accurately. Extending the duration of data collection will allow for a more comprehensive temporal analysis, facilitating the examination of seasonal patterns and the long-term effects of climate change. By incorporating climate change projections and increasing the dataset's size and scope, future models can provide more in-depth and reliable predictions, thereby improving rockfall risk management strategies. Conceptualization, W.C.; Data curation, Y.-J.J., C.-S.H., M.-H.K. and W.C.; Formal analysis, K.A.N. and W.C.; Funding acquisition, W.C.; Investigation, K.A.N., Y.-J.J., C.-S.H., M.-H.K. and W.C.; Methodology, K.A.N. and W.C.; Project administration, W.C.; Resources, W.C.; Software, K.A.N. ; Supervision, W.C.; Validation, K.A.N. ; Visualization, K.A.N. ; Writing--original draft, K.A.N. and W.C.; Writing--review and editing, Y.-J.J., C.-S.H., M.-H.K. and W.C. All authors have read and agreed to the published version of the manuscript. This study was partially funded by the National Science and Technology Council (Taiwan) Research Projects under Grant Numbers NSTC 112-2121-M-027-001 and NSTC 113-2121-M-008-004. The data used in this study are not publicly available due to restrictions imposed by the data owner or source. Therefore, the data cannot be disseminated or shared as part of this publication. Interested researchers can request access to the data directly from the data owner or source, subject to their terms and conditions. The authors confirm that they do not have the right to distribute the data used in this study. We would like to thank [PERSON] for his assistance in geolocating some of the rockfall sites for this study.