Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better?

Siyuan Ma; Xiaoyi Shao; Chong Xu

doi:10.1007/s12583-021-1407-1

Volume 34 Issue 2

Apr 2023

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Journal of Earth Science > 2023 > 34(2): 386-397.

Siyuan Ma, Xiaoyi Shao, Chong Xu. Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better?. Journal of Earth Science, 2023, 34(2): 386-397. doi: 10.1007/s12583-021-1407-1

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Siyuan Ma, Xiaoyi Shao, Chong Xu. Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better?. Journal of Earth Science, 2023, 34(2): 386-397. doi: 10.1007/s12583-021-1407-1

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Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better?

doi: 10.1007/s12583-021-1407-1

Siyuan Ma^1
,,
Xiaoyi Shao^{1, 2},
Chong Xu^{2, 3
,
,
,}

1.
Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing 100029, China
2.
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
3.
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China

More Information

Corresponding author: Chong Xu, xc11111111@126.com
Received Date: 08 Sep 2022
Accepted Date: 30 Dec 2022
Issue Publish Date: 30 Apr 2023

Abstract

Abstract

Choice of appropriate mapping units is important in landslide susceptibility mapping (LSM). There are various possible units for this choice, while it remains unclear which one is better in performance. The purpose of this study is to make a quantitative comparison of two commonly-used units: slope-unit (SU) and raster-unit (RU) based on the landslides triggered by the 2013 Minxian, Gansu, China M_w 5.9 earthquake. Ten landslide influencing factors were considered in this analysis. For each type of mapping units, the 70% samples were randomly selected and trained 20 times on the LR model, yielding 20 susceptibility maps, and the remaining 30% samples were tested for the accuracy of the modeling outcome. Different metrics, including the mean probability, model uncertainty, and model prediction skills, were used to evaluate the quality of the susceptibility maps. The results show that the resultant probability maps using two mapping units can largely predict the distribution of actual landslides, on which the high susceptibility area corresponds to the landslide-prone area. The AUC (area under curve) values, ranging from 0.8 to 0.86, show that the prediction ability of two mapping units is roughly the same. While comparing with the RU, the use of SU can lower the model uncertainties caused by the variation of training sets. We converted the RU-based assessment results into SU-based scheme. The results show that two assessment results are well fitted with good linear relationship, which implies that it is feasible to convert the RU-based landslide susceptibility mapping into the SU-based scheme. This analysis indicates that compared with the RU, the SU cannot improve the performance and accuracy of seismic landslide susceptibility mapping.
- earthquake-triggered landslides,
- Minxian earthquake,
- logistic regression (LR) model,
- landslide susceptibility,
- mapping unit,
- engineering geology

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References(49)

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