journal article·2025-07-23Open access

An Integrated Risk Assessment of Rockfalls Along Highway Networks in Mountainous Regions: The Case of Guizhou, China

Jinchen Yang, Zhiwen Xu, Mei Gong, Suhua Zhou, Minghua Huang

Applied Sciences· Volume 15· Issue 15

Abstract

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Landslides and related hazards · Geotechnical Engineering and Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Civil Engineering, Hunan University, Changsha 410082, China

College of Civil Engineering, Xiangtan University, Xiangtan 411105, China

National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha 410082, China

Guizhou Traffic Construction Quality Supervision, Guiyang 550008, China

Funding

Science and Technology Program of Guizhou Provincial Department of Transportation · Science and Technology Infrastructure Program of Guizhou Province · Natural Science Foundation of China · Natural Resources Science and Technology Project of Fujian Province · Postgraduate Scientific Research Innovation Project of Hunan Province

References (61)

Show source-provided references
1. The Varnes classification of landslide types, an update10.1007/s10346-013-0436-y ↗
2. Rockfall activity from an alpine cliff during thawing periods10.1016/S0169-555X(98)00116-0 ↗
3. Characteristics and susceptibility zonation of landslides in Wabe Shebelle Gorge, south eastern Ethiopia10.1016/j.jafrearsci.2021.104275 ↗
4. Landslides in a changing climate10.1016/j.earscirev.2016.08.011 ↗
5. Flood impacts on urban transit and accessibility—A case study of Kinshasa10.1016/j.trd.2021.102889 ↗
6. The influence of land urbanization on landslides: An empirical estimation based on Chinese provincial panel data10.1016/j.scitotenv.2017.03.258 ↗
7. Assessing and enhancing urban road network resilience under rainstorm waterlogging disasters10.1016/j.trd.2023.103928 ↗
8. Rockfall hazard estimation and related applications for a preliminary risk assessment at regional scale: An example from northwestern Italian Alps10.1007/s10346-023-02060-4 ↗
9. Climate change research on transportation systems: Climate risks, adaptation and planning10.1016/j.trd.2020.102553 ↗
10. Rockfall susceptibility and network-ranked susceptibility along the Italian railway10.1016/j.enggeo.2021.106301 ↗
11. Investigating the influences of precipitation, snowmelt, and freeze-thaw on rockfall in Glenwood Canyon, Colorado using terrestrial laser scanning10.1007/s10346-024-02266-0 ↗
12. The synthetic geo-ecological environmental evaluation of a coastal coal-mining city using spatiotemporal big data: A case study in Longkou, China10.1016/j.jclepro.2016.07.011 ↗
13. Liu, W., Zhang, Y., Liang, Y., Sun, P., Li, Y., Su, X., Wang, A., and Meng, X. (2022). Landslide risk assessment using a combined approach based on InSAR and random forest. Remote Sens., 14.10.3390/rs14092131 ↗
14. An empirical approach for developing functions for the vulnerability of roads to tropical cyclones10.1016/j.trd.2021.103136 ↗
15. Study on mobility-disadvantage group’risk perception and coping behaviors of abrupt geological hazards in coastal rural area of China10.1016/j.envres.2016.04.016 ↗
16. Global overview of the geological hazard exposure and disaster risk awareness at world heritage sites10.1016/j.culher.2015.11.001 ↗
17. Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques10.1016/j.enggeo.2023.107342 ↗
18. Wang, X., Liu, H., and Sun, J. (2021). A new approach for identification of potential rockfall source areas controlled by rock mass strength at a regional scale. Remote Sens., 13.10.3390/rs13050938 ↗
19. Su, Q., Zhang, J., Zhao, S., Wang, L., Liu, J., and Guo, J. (2017). Comparative assessment of three nonlinear approaches for landslide susceptibility mapping in a coal mine area. ISPRS Int. J. Geo-Inf., 6.10.3390/ijgi6070228 ↗
20. The indispensable role of resilience in rational landslide risk management for social sustainability
21. Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia10.1016/j.gsf.2019.07.006 ↗
22. The integrated assessment system of eco-geological environmental carrying capacity for Dujiangyan-Wenchaun highway10.1016/j.apsusc.2015.10.234 ↗
23. Jia, L., Wang, J., Gao, S., Fang, L., and Wang, D. (2023). Landslide risk evaluation method of open-pit mine based on numerical simulation of large deformation of landslide. Sci. Rep., 13.10.1038/s41598-023-42736-4 ↗
24. Yu, C., and Chen, J. (2020). Application of a GIS-based slope unit method for landslide susceptibility mapping in Helong City: Comparative assessment of ICM, AHP, and RF model. Symmetry, 12.10.3390/sym12111848 ↗
25. Assessing landslide susceptibility along India’s National Highway 58: A comprehensive approach integrating remote sensing, GIS, and logistic regression analysis10.3390/conservation3030030 ↗
26. Ali, Z., Dahri, N., Vanclooster, M., Mehmandoostkotlar, A., Labbaci, A., Ben Zaied, M., and Ouessar, M. (2023). Hybrid Fuzzy AHP and Frequency Ratio Methods for Assessing Flood Susceptibility in Bayech Basin, Southwestern Tunisia. Sustainability, 15.10.3390/su152115422 ↗
27. Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage10.1016/j.ecolind.2023.111179 ↗
28. A python system for regional landslide susceptibility assessment by integrating machine learning models and its application10.1016/j.heliyon.2023.e21542 ↗
29. Lu, J., Ren, C., Yue, W., Zhou, Y., Xue, X., Liu, Y., and Ding, C. (2023). Investigation of landslide susceptibility decision mechanisms in different ensemble-based machine learning models with various types of factor data. Sustainability, 15.10.3390/su151813563 ↗
30. Tang, H., Wang, C., An, S., Wang, Q., and Jiang, C. (2023). A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping. Remote Sens., 15.10.3390/rs15174159 ↗
31. Geographically weighted neural network considering spatial heterogeneity for landslide susceptibility mapping: A case study of Yichang City, China10.1016/j.catena.2023.107590 ↗
32. Land use change and eco-environmental vulnerability evaluation in the Danjiangkou Reservoir Area
33. Geological hazard risk assessment based on information quantity model in Fuling District, Chongqing City. China
34. Ruan, Y., Huo, R., Chen, J., Liu, W., Zhou, X., Wang, T., Hou, M., and Huang, W. (2023). Assessing the Susceptibility of Landslides in the Tuoding Section of the Upper Reaches of the Jinsha River, China, Using a Combination of Information Quantity Modeling and GIS. Water, 15.10.3390/w15203685 ↗
35. Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province, China10.1007/s11629-022-7698-6 ↗
36. Yang, H., Wu, Q., Dong, J., Xie, F., and Zhang, Q. (2023). Landslide risk mapping using the weight-of-evidence method in the datong mining area, qinghai province. Sustainability, 15.10.3390/su151411330 ↗
37. Zhang, Y., Deng, L., Han, Y., Sun, Y., Zang, Y., and Zhou, M. (2023). Landslide hazard assessment in highway areas of Guangxi using remote sensing data and a pre-trained XGBoost model. Remote Sens., 15.10.3390/rs15133350 ↗
38. Chaoyong, S., Shaoqi, Z., Xuling, L., Yu, Z., and Hui, L. (2023). Using DInSAR to inventory landslide geological disaster in Bijie, Guizhou, China. Front. Earth Sci., 10.10.3389/feart.2022.1024710 ↗
39. Chen, L., Zhao, C., Chen, H., Kang, Y., Li, B., and Liu, X. (2023). The Detection and Control Factor Analysis of Active Landslides in Guizhou Province, China, Using Sentinel-1 SAR Imagery. Remote Sens., 15.10.3390/rs15235468 ↗
40. Risk assessment of roadway networks exposed to landslides in mountainous regions—A case study in Fengjie County, China10.1007/s10346-023-02045-3 ↗
41. Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment10.1016/j.jclepro.2021.127077 ↗
42. Increasing costs to Chinese railway infrastructure by extreme precipitation in a warmer world10.1016/j.trd.2021.102797 ↗
43. Wang, Y., Wen, H., Sun, D., and Li, Y. (2021). Quantitative assessment of landslide risk based on susceptibility mapping using random forest and geodetector. Remote Sens., 13.10.3390/rs13132625 ↗
44. Crawford, M.M., Dortch, J.M., Koch, H.J., Zhu, Y., Haneberg, W.C., Wang, Z., and Bryson, L.S. (2022). Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach. Remote Sens., 14.10.3390/rs14246246 ↗
45. Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran10.1007/s10661-022-10206-5 ↗
46. Landslide risk assessment considering socionatural factors: Methodology and application to Cubatão municipality, São Paulo, Brazil10.1007/s11069-021-04991-4 ↗
47. Landslide risk evaluation based on slope unit: A case on the Western Hubei area, China10.1007/s12517-022-10319-8 ↗
48. Landslide risk evaluation and its causative factors in typical mountain environment of China: A case study of Yunfu City10.1016/j.ecolind.2023.110821 ↗
49. Landslide risk of regional roads: Consider the road mileage of expected losses10.1016/j.trd.2023.103771 ↗
50. Exploring the spatial patterns of landslide susceptibility assessment using interpretable Shapley method: Mechanisms of landslide formation in the Sichuan-Tibet region10.1016/j.jenvman.2024.121921 ↗
51. A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm10.1016/j.compgeo.2024.106106 ↗
52. Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China
53. A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network10.1016/j.gr.2024.04.013 ↗
54. Assessing Landslide susceptibility and formulating adaptation strategies in the Konto Watershed, East Java, Indonesia10.1016/j.ijdrr.2024.104797 ↗
55. Qiu, H., Xu, Y., Tang, B., Su, L., Li, Y., Yang, D., and Ullah, M. (2024). Interpretable Landslide Susceptibility Evaluation Based on Model Optimization. Land, 13.10.3390/land13050639 ↗
56. Geotechnical characterization and landslides in the weathered granitoids of Calabria (southern Italy)
57. Geotechnical and Landslide Aspects in Weathered Granitoid Rock Masses (Serre Massif, Southern Calabria, Italy)10.1016/j.catena.2016.06.027 ↗
58. Liu, Y., Zhang, W., Zhang, Z., Xu, Q., and Li, W. (2021). Risk factor detection and landslide susceptibility mapping using Geo-Detector and Random Forest Models: The 2018 Hokkaido eastern Iburi earthquake. Remote Sens., 13.10.3390/rs13061157 ↗
59. Social vulnerability assessment of landslide disaster based on improved TOPSIS method: Case study of eleven small towns in China10.1016/j.ecolind.2022.109316 ↗
60. Vulnerability Evaluation of Rainstorm Disaster Based on ESA Conceptual Framework: A Case Study of Liaoning Province, China
61. Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China10.1016/j.jrmge.2023.09.037 ↗