Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways

Kieu Anh Nguyen, Yi-Jia Jiang, Chiao-Shin Huang, Meng-Hsun Kuo and Walter Chen ()
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Kieu Anh Nguyen: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Yi-Jia Jiang: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Chiao-Shin Huang: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Meng-Hsun Kuo: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Walter Chen: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

Future Internet, 2024, vol. 16, issue 8, 1-18

Abstract: Over three-quarters of Taiwan’s landmass consists of mountainous slopes with steep gradients, leading to frequent rockfall hazards that obstruct traffic and cause injuries and fatalities. This study used Google Alerts to compile internet news on rockfall incidents along Taiwan’s highway system from April 2019 to February 2024. The locations of these rockfalls were geolocated using Google Earth and integrated with geographical, topographical, environmental, geological, and socioeconomic variables. Employing machine learning algorithms, particularly the Random Forest algorithm, we analyzed the potential for rockfall hazards along roadside slopes. The model achieved an overall accuracy of 0.8514 on the test dataset, with a sensitivity of 0.8378, correctly identifying 83.8% of rockfall locations. Shapley Additive Explanations (SHAP) analysis highlighted that factors such as slope angle and distance to geologically sensitive areas are pivotal in determining rockfall locations. The study underscores the utility of internet-based data collection in providing comprehensive coverage of Taiwan’s highway system, and enabled the first broad analysis of rockfall hazard susceptibility for the entire highway network. The consistent importance of topographical and geographical features suggests that integrating detailed spatial data could further enhance predictive performance. The combined use of Random Forest and SHAP analyses offers a robust framework for understanding and improving predictive models, aiding in the development of effective strategies for risk management and mitigation in rockfall-prone areas, ultimately contributing to safer and more reliable transportation networks in mountainous regions.

Keywords: rockfalls; highways; machine learning; random forest; Taiwan (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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