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2024 Vol. 44, No. 3
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SUN Cai, TIE Yongbo, NING Zhijie, XU Wei, XIONG Xiaohui. 2024. Landslide susceptibility mapping in Xide County, Sichuan Province based on frequency ratio-support vector machine coupling model. Sedimentary Geology and Tethyan Geology, 44(3): 547-559. doi: 10.19826/j.cnki.1009-3850.2024.08002
Citation: SUN Cai, TIE Yongbo, NING Zhijie, XU Wei, XIONG Xiaohui. 2024. Landslide susceptibility mapping in Xide County, Sichuan Province based on frequency ratio-support vector machine coupling model. Sedimentary Geology and Tethyan Geology, 44(3): 547-559. doi: 10.19826/j.cnki.1009-3850.2024.08002
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Landslide susceptibility mapping in Xide County, Sichuan Province based on frequency ratio-support vector machine coupling model

  • 1.

    Chongqing Jiaotong University, College of River and Ocean Engineering, Chongqing 400074, China

  • 2.

    Chengdu Center of China Geological Survey (Geosciences Innovation Center of Southwest China), Chengdu 610218, China

  • 3.

    Technology Innovation Center of Risk Prevention and Mitigation of Geohazard, Ministry of Nature Resources, Chengdu 611734, China

  • 4.

    Observation and Research Station of Chengdu Geological Hazards, Ministry of Nature Resources, Chengdu 610000, China

  • 5.

    China University of Geosciences (Beijing), Chinese Academy of Geological Sciences, Beijing 100083, China

More Information
Received Date:  23 May 2024
Revised Date:  13 August 2024
Accepted Date:  14 August 2024
Publish Date:  30 September 2024
    Abstract

  • This study addresses the critical issue of selecting factor classification base data and evaluation models for landslide susceptibility mapping, focusing on Xide County in Sichuan Province, a region frequently affected by landslide hazards. Utilizing slope units as evaluation units, a correlation analysis of the evaluation factors was conducted, ultimately selecting eleven key factors: elevation, slope angle, curvature, normalized difference vegetation index (NDVI), stream power index (SPI), distance to watercourses, distance to roads, distance to faults, slope structure, engineering geological rock groups, and land use types. Factors were classified using the natural breaks method for both regional point attributes and landslide point attributes. These classified factors were then incorporated into the frequency ratio model and the frequency ratio-support vector machine coupled model to evaluate landslide susceptibility. The precision of these models was validated using receiver operating characteristic (ROC) curves and typical slope analysis. The findings revealed that using landslide-specific attributes as the classification base data within the coupled model framework yielded the highest evaluation accuracy, with an area under the ROC curve (SAUC) value of 0.752, indicating a superior predictive capability for landslide susceptibility. The simulation results indicated that areas of extremely high and high susceptibility constitute 4.65% and 23.73% of the study area, respectively, predominantly located in regions characterized by significant topographic relief, well-developed faults, and intense human engineering activities. Conversely, regions with sparse faults and low population density were categorized as medium and low susceptibility zones, accounting for 44.20% and 27.42% of the study area, respectively. These findings provide essential scientific insights and references for the effective assessment and management of landslide susceptibility in Xide County and other similar regions.

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