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Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model

Author

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  • Ruixia Ma

    (School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710000, China)

  • Yan Lyu

    (School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710000, China)

  • Tianbao Chen

    (Chongqing Shutong Geotechnical Engineering Co., Ltd., Chongqing 401147, China)

  • Qian Zhang

    (School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710000, China)

Abstract

Qinglong Gorge Scenic Area (QGSA) boasts stunning natural landscapes, characterized by towering peaks and extensive cliffs. Nevertheless, the intricate geological backdrop and distinctive topographical conditions of this area give rise to various geological disasters, posing a substantial safety concern for tourists and presenting ongoing operational and safety management challenges for the scenic area. In light of these challenges, this study placed its focus on the geological disasters within QGSA and sought to assess risks across various scales. The assessment was accomplished through a combination of methods, including field surveys conducted in 2022, remote sensing interpretation, and comprehensive data collection and organization. For the geological disaster risk assessment of the scenic area, this research selected seven key indicators, encompassing terrain factors, geological elements, structural characteristics, and other relevant factors. The assessment utilized a logistic regression model, which yielded satisfactory results with an AUC value of 0.8338. Furthermore, a model was constructed incorporating seven indicators, encompassing factors such as population vulnerability, material susceptibility, and the vulnerability of tourism resources. To assess vulnerability to geological disasters, the Analytic Hierarchy Process (AHP) was employed, resulting in a CR of 0, thus ensuring the reliability of the findings. The outcomes of the risk assessment indicate that the low-risk area covers a substantial expanse of 5.45 km 2 , representing 53.66% of the total area. The moderate-risk area extends over 3.59 km 2 , constituting 35.43%, while the high-risk area encompasses 0.72 km 2 , accounting for 7.14%. Additionally, the very high-risk area encompasses 0.38 km 2 , making up 3.77% of the total area. Consequently, building upon the findings of the risk assessment, this paper introduces a risk classification and control prevention system. This system provides invaluable insights for disaster prevention and control in mountainous and canyon-type scenic areas.

Suggested Citation

  • Ruixia Ma & Yan Lyu & Tianbao Chen & Qian Zhang, 2023. "Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model," Sustainability, MDPI, vol. 15(22), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15752-:d:1276399
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    References listed on IDEAS

    as
    1. Shaohan Zhang & Shucheng Tan & Jinxuan Zhou & Yongqi Sun & Duanyu Ding & Jun Li, 2023. "Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    2. He Yang & Qihong Wu & Jianhui Dong & Feihong Xie & Qixue Zhang, 2023. "Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    3. Shaohan Zhang & Shucheng Tan & Hui Geng & Ronwei Li & Yongqi Sun & Jun Li, 2023. "Evaluation of Geological Hazard Risk in Yiliang County, Yunnan Province, Using Combined Assignment Method," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
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