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Seismic landslide susceptibility assessment using principal component analysis and support vector machine.

Ziyao Xu1, Ailan Che2, Hanxu Zhou1

  • 1School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.

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This study developed a new model to assess earthquake-triggered landslide susceptibility in data-scarce regions. The model accurately identified high-risk areas along the Dayong highway, improving disaster preparedness.

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Area of Science:

  • Geosciences
  • Earthquake Engineering
  • Natural Hazard Assessment

Background:

  • Seismic landslides pose significant risks to infrastructure and human safety.
  • Accurate landslide susceptibility assessment is crucial for disaster management but often hindered by limited historical landslide data.
  • Geographical and human factors can obscure evidence of past landslide activities, complicating assessments.

Purpose of the Study:

  • To establish a generalized seismic landslide susceptibility assessment model applicable to areas with limited landslide data.
  • To apply this model to the Dayong highway in the Chenghai area, a region prone to frequent earthquakes.
  • To provide a novel approach for seismic landslide susceptibility assessment in data-deficient regions.

Main Methods:

  • Utilized landslide data from a similar geographical region (2014 Ludian earthquake).
  • Employed the frequency ratio method to filter insignificant influencing factors.
  • Applied Principal Component Analysis (PCA) for dimensionality reduction and Support Vector Machine (SVM) for model construction (PCA-SVM).

Main Results:

  • The PCA-SVM model achieved a high accuracy of 93.6%.
  • Landslide susceptibility in the Chenghai area was classified into five levels, with "Very high" susceptibility covering 0.63%.
  • A 13-km section of the Dayong highway was identified as a high-risk zone, significantly impacted by seismic landslides.

Conclusions:

  • The developed PCA-SVM model offers a reliable method for seismic landslide susceptibility assessment in data-scarce environments.
  • The study successfully identified critical high-risk areas along the Dayong highway, informing targeted disaster mitigation strategies.
  • This research presents a transferable methodology for seismic landslide hazard evaluation in similar geographical contexts.