Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models
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 fa...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/6/377 |
| _version_ | 1797565817902071808 |
|---|---|
| author | Faming Huang Jiawu Chen Zhen Du Chi Yao Jinsong Huang Qinghui Jiang Zhilu Chang Shu Li |
| author_facet | Faming Huang Jiawu Chen Zhen Du Chi Yao Jinsong Huang Qinghui Jiang Zhilu Chang Shu Li |
| author_sort | Faming Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:18:23Z |
| format | Article |
| id | doaj.art-de532c3804b842c49e4c5b41b8c21f0e |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T19:18:23Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-de532c3804b842c49e4c5b41b8c21f0e2023-11-20T03:13:26ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-06-019637710.3390/ijgi9060377Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning ModelsFaming Huang0Jiawu Chen1Zhen Du2Chi Yao3Jinsong Huang4Qinghui Jiang5Zhilu Chang6Shu Li7School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaSchool of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, ChinaChangjiang Institute of Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, ChinaSoil 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.https://www.mdpi.com/2220-9964/9/6/377landslide susceptibility predictionsoil erosionpredisposing factorssupport vector machineC5.0 decision tree |
| spellingShingle | Faming Huang Jiawu Chen Zhen Du Chi Yao Jinsong Huang Qinghui Jiang Zhilu Chang Shu Li Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models ISPRS International Journal of Geo-Information landslide susceptibility prediction soil erosion predisposing factors support vector machine C5.0 decision tree |
| title | Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models |
| title_full | Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models |
| title_fullStr | Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models |
| title_full_unstemmed | Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models |
| title_short | Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models |
| title_sort | landslide susceptibility prediction considering regional soil erosion based on machine learning models |
| topic | landslide susceptibility prediction soil erosion predisposing factors support vector machine C5.0 decision tree |
| url | https://www.mdpi.com/2220-9964/9/6/377 |
| work_keys_str_mv | AT faminghuang landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT jiawuchen landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT zhendu landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT chiyao landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT jinsonghuang landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT qinghuijiang landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT zhiluchang landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels AT shuli landslidesusceptibilitypredictionconsideringregionalsoilerosionbasedonmachinelearningmodels |