Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation
The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information ab...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.993975/full |
| _version_ | 1828070670062321664 |
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| author | Yu Zhao Yu Zhao Zeng Huang Zeng Huang Zhenlei Wei Jun Zheng Kazuo Konagai |
| author_facet | Yu Zhao Yu Zhao Zeng Huang Zeng Huang Zhenlei Wei Jun Zheng Kazuo Konagai |
| author_sort | Yu Zhao |
| collection | DOAJ |
| description | The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information about the distance to the surface rupture is lacking. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regarded as the study case. To select a more suitable model for generating the landslide susceptibility map, three commonly used models named logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) were also conducted to assess landslide susceptibility. The performances of these three models were evaluated with the receiver operating characteristic curve. The calculated results showed that the ANN model has the highest area under the curve (AUC) 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 susceptibility analysis at two different mapping scales (the whole study area and the epicenter area) was also applied. |
| first_indexed | 2024-04-11T00:41:27Z |
| format | Article |
| id | doaj.art-72baa2d404604f078a5420b18a505fdc |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-11T00:41:27Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-72baa2d404604f078a5420b18a505fdc2023-01-06T05:39:09ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.993975993975Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformationYu Zhao0Yu Zhao1Zeng Huang2Zeng Huang3Zhenlei Wei4Jun Zheng5Kazuo Konagai6College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaMOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaPowerchina Zhongnan Engineering Corporation Limited, Changsha, ChinaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaInternational Consortium on Landslides, Kyoto, JapanThe distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information about the distance to the surface rupture is lacking. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regarded as the study case. To select a more suitable model for generating the landslide susceptibility map, three commonly used models named logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) were also conducted to assess landslide susceptibility. The performances of these three models were evaluated with the receiver operating characteristic curve. The calculated results showed that the ANN model has the highest area under the curve (AUC) 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 susceptibility analysis at two different mapping scales (the whole study area and the epicenter area) was also applied.https://www.frontiersin.org/articles/10.3389/feart.2022.993975/fullearthquake-triggered landslideslandslide susceptibility mappingcoseismic ground deformationmachine learningburied-fault earthquakesMid-Niigata earthquake |
| spellingShingle | Yu Zhao Yu Zhao Zeng Huang Zeng Huang Zhenlei Wei Jun Zheng Kazuo Konagai Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation Frontiers in Earth Science earthquake-triggered landslides landslide susceptibility mapping coseismic ground deformation machine learning buried-fault earthquakes Mid-Niigata earthquake |
| title | Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation |
| title_full | Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation |
| title_fullStr | Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation |
| title_full_unstemmed | Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation |
| title_short | Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation |
| title_sort | assessment of earthquake triggered landslide susceptibility considering coseismic ground deformation |
| topic | earthquake-triggered landslides landslide susceptibility mapping coseismic ground deformation machine learning buried-fault earthquakes Mid-Niigata earthquake |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.993975/full |
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