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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Mar 2020

Submitted as: research article | 16 Mar 2020

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This preprint is currently under review for the journal NHESS.

The assessment of earthquake-triggered landslides susceptibility with considering coseismic ground deformation

Yu Zhao1,2, Zeng Huang1, Zhenlei Wei1, Jun Zheng1, and Kazuo Konagai3 Yu Zhao et al.
  • 1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
  • 2MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
  • 3International consortium on landslides, Kyoto 611-0011, Japan

Abstract. The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslides susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquakes cases, which mean lacking of the information about the distance to the surface rupture. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regareded as the study case. In order to select a more suitable model for generating the landslides susceptibility map, three commonly used models named Logistic Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were also conducted to assess the landslides susceptibility. The performances of these three models were evaluated with the receiver operating characteristic (ROC) curve. The calculated results showed the ANN model has the highest AUC (area under the curve) value of 0.82. As the earthquake triggered more landslides in the epicenter area, which makes it more prone to landslides in further earthquakes, the landslides susceptibility in the epicenter area was also further evaluated.

Yu Zhao et al.

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Yu Zhao et al.

Yu Zhao et al.


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