浙西梅雨滑坡易发性评价模型对比

冯杭建; 周爱国; 俞剑君; 唐小明; 郑嘉丽; 陈秀秀; 游省易

doi:10.3799/dqkx.2016.032

Volume 41 Issue 3

Mar. 2016

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Article Navigation > Earth Science > 2016 > 41(3): 403-415

Feng Hangjian, Zhou Aiguo, Yu Jianjun, Tang Xiaoming, Zheng Jiali, Chen Xiuxiu, You Shengyi, 2016. A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province. Earth Science, 41(3): 403-415. doi: 10.3799/dqkx.2016.032

Citation:

Feng Hangjian, Zhou Aiguo, Yu Jianjun, Tang Xiaoming, Zheng Jiali, Chen Xiuxiu, You Shengyi, 2016. A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province. Earth Science, 41(3): 403-415. doi: 10.3799/dqkx.2016.032

Citation:

Feng Hangjian, Zhou Aiguo, Yu Jianjun, Tang Xiaoming, Zheng Jiali, Chen Xiuxiu, You Shengyi, 2016. A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province. Earth Science, 41(3): 403-415. doi: 10.3799/dqkx.2016.032

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A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province

doi: 10.3799/dqkx.2016.032

1.
Institute of geological survey, China University of Geosciences, Wuhan 430074, China
2.
Zhejiang Institute of Geology and Mineral Resources, Hangzhou 310007, China
3.
Environmental Change Institute, School of Geography and Environment, University of Oxford OX1 3QY, UK
4.
Department of Earth Science, Zhejiang University, Hangzhou 310027, China

Received Date: 2015-09-10
Publish Date: 2016-03-15

Abstract

Abstract

A plenty of landslide susceptibility mapping studies in south west China have been reported in literatures. However, the assessment studies of rainfall-triggered landslides in south east China are still limited, particularly for those dominated by plum rains. Based on GIS and grid analysis, a study case in Cunan county is selected for demonstrating the comparison of applying three methods for landslide susceptibility assessment. They include artificial neural networks (ANN), logistic regression (LGR) and information model (IFM). The landslide inventory includes totally 596 landslides, which is established based on the results of remote sensing interpretation and detail survey. Totally 32 models are established by altering different combinations of controlling factors (CFs) out of totally 9 factors, including elevation, slope angle, slope aspect, slope curvature, lithology, distance from faults, distance from roads, distance from construction lands and vegetation. The indicator of Area Under Curve (AUC) is used for model evaluation. The ANN model could achieve the AUC of 93.75%, which outperforms LGR and IFM with the AUC of 89.76% and 90.06%, respectively. It also performs well in prediction to achieve the AUC of 94.75% compared to those (i.e. 94.33% and 77.21%) from LGR and IFM, where 13 landslides occurred in 2014 during plum-rain season are used for verification. The results of susceptibility zoning based on the derived susceptibility map using ANN also show reasonable, indicating the increases of landslide intensity with the increasing of susceptibility levels. Overall, this study demonstrates the best practices of applying different methods in rainfall-triggered landslide susceptibility assessment, which could be the reference for similar studies elsewhere in west Zhejiang province.
- susceptibility assessment,
- plum rain,
- landslides,
- artificial neural networks,
- logistic regression,
- information model

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References(80)

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province

doi: 10.3799/dqkx.2016.032

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通讯作者: 陈斌, bchen63@163.com

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