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Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China

Author

Listed:
  • Wei Lin

    (China University of Geosciences)

  • Kunlong Yin

    (China University of Geosciences)

  • Ningtao Wang

    (China Geological Survey (Central South China Innovation Center for Geosciences))

  • Yong Xu

    (China Geological Survey (Central South China Innovation Center for Geosciences))

  • Zizheng Guo

    (China University of Geosciences)

  • Yuanyao Li

    (China University of Geosciences)

Abstract

The traditional Stability INdex MAPping (SINMAP) model does not perform detailed divisions of study areas and neglects differences caused by the asymmetrical spatial distribution of geotechnical parameters; thus, the accuracy of the evaluation results is insufficient. In this study, the evaluation results of the SINMAP model were improved based on a combination with the certainty factor (CF) model, and the proposed method is referred to as the CF-SINMAP model. The Wuling Mountain area in Cili County of Hunan Province (China) was selected to verify the CF-SINMAP model. First, eight geological environmental factors in the region were analyzed by the CF method, including the slope, distance from fault, slope direction, distance from water, rock and soil type, elevation, distance from road and vegetation coverage. The rock and soil type, vegetation coverage and human engineering activities were determined as the key factors underlying landslide hazards. Then, the study area was divided into six regions based on the key factors, and the physical and mechanical parameters of each region were refined by the natural environment, formation lithology and human activities. Finally, the CF-SINMAP model was used to calculate and analyze the landslide hazard assessment results under different rainfall conditions. The results show that the CF-SINMAP model is more sensitive to rainfall compared with the traditional method and the unstable areas are mainly distributed along river valleys, reservoir banks and areas with continual human engineering activities. The area under the receiver operating characteristic (ROC) curve values was 0.75 and 0.61 for the CF-SINMAP and SINMAP models, respectively. Compared with the traditional SINMAP model, the CF-SINMAP model produces more reliable results. The rainfall threshold that induced the landslide disaster in Cili County, Hunan Province, was 90 mm/d. In summary, the CF-SINMAP model provides new ideas for the prediction of regional rainfall-induced landslides.

Suggested Citation

  • Wei Lin & Kunlong Yin & Ningtao Wang & Yong Xu & Zizheng Guo & Yuanyao Li, 2021. "Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 679-700, March.
    • Handle: RePEc:spr:nathaz:v:106:y:2021:i:1:d:10.1007_s11069-020-04483-x
    DOI: 10.1007/s11069-020-04483-x
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    References listed on IDEAS

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    1. Salvatore Rampone & Alessio Valente, 2012. "Neural Network Aided Evaluation Of Landslide Susceptibility In Southern Italy," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(01), pages 1-20.
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    Cited by:

    1. Lanqian Feng & Mingming Guo & Wenlong Wang & Yulan Chen & Qianhua Shi & Wenzhao Guo & Yibao Lou & Hongliang Kang & Zhouxin Chen & Yanan Zhu, 2022. "Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    2. Xianyu Yu & Tingting Xiong & Weiwei Jiang & Jianguo Zhou, 2023. "Comparative Assessment of the Efficacy of the Five Kinds of Models in Landslide Susceptibility Map for Factor Screening: A Case Study at Zigui-Badong in the Three Gorges Reservoir Area, China," Sustainability, MDPI, vol. 15(1), pages 1-26, January.
    3. Chi Yang & Jinghan Wang & Shuyi Li & Ruihan Xiong & Xiaobo Li & Lin Gao & Xu Guo & Chuanming Ma & Hanxiang Xiong & Yang Qiu, 2024. "Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China," Sustainability, MDPI, vol. 16(11), pages 1-26, May.
    4. Chenchen Xie & Yuandong Huang & Lei Li & Tao Li & Chong Xu, 2023. "Detailed Inventory and Spatial Distribution Analysis of Rainfall-Induced Landslides in Jiexi County, Guangdong Province, China in August 2018," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    5. Zohre Hoseinzade & Asal Zavarei & Kourosh Shirani, 2021. "Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2489-2507, December.

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