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A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model

Author

Listed:
  • Huadan Fan

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)

  • Yuefeng Lu

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Yulong Hu

    (China Transport Telecommunications & Information Center, Beijing 100011, China)

  • Jun Fang

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Chengzhe Lv

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)

  • Changqing Xu

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)

  • Xinyi Feng

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)

  • Yanru Liu

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)

Abstract

A landslide disaster, especially a highway landslide, may greatly impact the transport capacity of nearby roads. Keeping highways open, in particular, is crucial for supporting the functioning of the economy, society and people. Therefore, evaluating the highway landslide susceptibility is particularly important. In this paper, the city of Laibin, in the Guangxi Zhuang Autonomous Region of China, was taken as the study zone. According to data on 641 highway landslide disaster points measured in the field and a basic evaluation of the study area, nine evaluation factors—the elevation, slope, aspect, height difference, plan curve, profile curve, precipitation, Topographic Wetness Index (TWI) and vegetation coverage—were selected. We coupled a Frequency Ratio (FR) model, Analytic Hierarchy Process (AHP), Logistic Regression (LR), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to evaluate the susceptibility to highway landslides, with a Receiver Operating Characteristic (ROC) curve used to analyze the precision of these models. The ROC curve showed that the accuracy of the five models was greater than 0.700 and thus had a certain reliability. Among them, the FR-LR model had the highest accuracy, at 0.804. The study protocol presented here can therefore provide a reference for evaluation studies on landslide susceptibility in other areas.

Suggested Citation

  • Huadan Fan & Yuefeng Lu & Yulong Hu & Jun Fang & Chengzhe Lv & Changqing Xu & Xinyi Feng & Yanru Liu, 2022. "A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7740-:d:847139
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    References listed on IDEAS

    as
    1. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    2. Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    3. Binh Thai Pham & Indra Prakash & Wei Chen & Hai-Bang Ly & Lanh Si Ho & Ebrahim Omidvar & Van Phong Tran & Dieu Tien Bui, 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 11(22), pages 1-30, November.
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    Cited by:

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    2. Jiakai Lu & Chao Ren & Weiting Yue & Ying Zhou & Xiaoqin Xue & Yuanyuan Liu & Cong Ding, 2023. "Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data," Sustainability, MDPI, vol. 15(18), pages 1-49, September.
    3. Shuhang Li & Mohamed Abdelkareem & Nassir Al-Arifi, 2023. "Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China," Land, MDPI, vol. 12(4), pages 1-20, March.
    4. Zefang Zhang & Zhikuan Qian & Yong Wei & Xing Zhu & Linjun Wang, 2022. "Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    5. Jing Li & Yuefeng Lu & Xiwen Li & Rui Wang & Ying Sun & Yanru Liu & Kaizhong Yao, 2023. "Evaluation and Analysis of Development Status of Yellow River Beach Area Based on Multi-Source Data and Coordination Degree Model," Sustainability, MDPI, vol. 15(7), pages 1-25, March.

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