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Distribution Pattern of Coseismic Landslides Triggered by the 2017 Jiuzhaigou Ms 7.0 Earthquake of China: Control of Seismic Landslide Susceptibility
Open AccessArticle

Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models

1
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
2
ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW 2308, Australia
3
Changjiang Institute of Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 377; https://doi.org/10.3390/ijgi9060377
Received: 20 March 2020 / Revised: 26 May 2020 / Accepted: 2 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions—C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)—are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models; C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor; (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions; and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models. View Full-Text
Keywords: landslide susceptibility prediction; soil erosion; predisposing factors; support vector machine; C5.0 decision tree landslide susceptibility prediction; soil erosion; predisposing factors; support vector machine; C5.0 decision tree
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Huang, F.; Chen, J.; Du, Z.; Yao, C.; Huang, J.; Jiang, Q.; Chang, Z.; Li, S. Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models. ISPRS Int. J. Geo-Inf. 2020, 9, 377.

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