Landslide susceptibility prediction using C5.0 decision tree model
Regional landslide susceptibility prediction (LSP) research is of great significance to the prevention and control of landslides. This study focuses on the LSP modelling based on the decision tree model. Taking the northern part of An’yuan County of Jiangxi Province as an example, 14 environmental f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/25/e3sconf_gesd2022_01015.pdf |
| _version_ | 1811189068498731008 |
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| author | Shua Qiangqiang Chen Xiaogang Lian Zhipeng Liu Gengzhe Tao Siyu |
| author_facet | Shua Qiangqiang Chen Xiaogang Lian Zhipeng Liu Gengzhe Tao Siyu |
| author_sort | Shua Qiangqiang |
| collection | DOAJ |
| description | Regional landslide susceptibility prediction (LSP) research is of great significance to the prevention and control of landslides. This study focuses on the LSP modelling based on the decision tree model. Taking the northern part of An’yuan County of Jiangxi Province as an example, 14 environmental factors including elevation, gully density and lithology are obtained based on geographical information system (GIS) and remote sensing satellite. Frequency Ratio method and C5.0 decision tree (DT) model are coupled to build DT model for LSP modelling. Then the predicted results are graded into five attribute intervals. Finally, LSP performance of DT model is evaluated by comparing the area value under the receiver operating characteristic curve (ROC) and classification of landslide susceptibility. The results show that the AUC accuracy of the C5.0 DT model is 0.805, and the LSP results of the C5.0 DT model are consistent with the actual distribution pattern of landslides in this County. |
| first_indexed | 2024-04-11T14:29:41Z |
| format | Article |
| id | doaj.art-444101d796d347df9c201aeb0c3267c8 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-11T14:29:41Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-444101d796d347df9c201aeb0c3267c82022-12-22T04:18:42ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013580101510.1051/e3sconf/202235801015e3sconf_gesd2022_01015Landslide susceptibility prediction using C5.0 decision tree modelShua Qiangqiang0Chen Xiaogang1Lian Zhipeng2Liu Gengzhe3Tao Siyu4School of Architecture Engineering, Sichuan Universit of Arts and ScienceSchool of Infrastructure Engineering, Nanchang UniversityWuhan Center, China Geological SurveyUniversity of Pennsylvania school of designSchool of Infrastructure Engineering, Nanchang UniversityRegional landslide susceptibility prediction (LSP) research is of great significance to the prevention and control of landslides. This study focuses on the LSP modelling based on the decision tree model. Taking the northern part of An’yuan County of Jiangxi Province as an example, 14 environmental factors including elevation, gully density and lithology are obtained based on geographical information system (GIS) and remote sensing satellite. Frequency Ratio method and C5.0 decision tree (DT) model are coupled to build DT model for LSP modelling. Then the predicted results are graded into five attribute intervals. Finally, LSP performance of DT model is evaluated by comparing the area value under the receiver operating characteristic curve (ROC) and classification of landslide susceptibility. The results show that the AUC accuracy of the C5.0 DT model is 0.805, and the LSP results of the C5.0 DT model are consistent with the actual distribution pattern of landslides in this County.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/25/e3sconf_gesd2022_01015.pdflandslide susceptibility predictionfrequency ratioc5.0 decision treegeographical information system |
| spellingShingle | Shua Qiangqiang Chen Xiaogang Lian Zhipeng Liu Gengzhe Tao Siyu Landslide susceptibility prediction using C5.0 decision tree model E3S Web of Conferences landslide susceptibility prediction frequency ratio c5.0 decision tree geographical information system |
| title | Landslide susceptibility prediction using C5.0 decision tree model |
| title_full | Landslide susceptibility prediction using C5.0 decision tree model |
| title_fullStr | Landslide susceptibility prediction using C5.0 decision tree model |
| title_full_unstemmed | Landslide susceptibility prediction using C5.0 decision tree model |
| title_short | Landslide susceptibility prediction using C5.0 decision tree model |
| title_sort | landslide susceptibility prediction using c5 0 decision tree model |
| topic | landslide susceptibility prediction frequency ratio c5.0 decision tree geographical information system |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/25/e3sconf_gesd2022_01015.pdf |
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