| Issue |
E3S Web Conf.
Volume 358, 2022
5th International Conference on Green Energy and Sustainable Development (GESD 2022)
|
|
|---|---|---|
| Article Number | 01015 | |
| Number of page(s) | 6 | |
| Section | Invited Contributions | |
| DOI | https://doi.org/10.1051/e3sconf/202235801015 | |
| Published online | 27 October 2022 | |
Landslide susceptibility prediction using C5.0 decision tree model
1 School of Architecture Engineering, Sichuan Universit of Arts and Science, Dazhou City, China
2 School of Infrastructure Engineering, Nanchang University, Nanchang, China
3 Wuhan Center, China Geological Survey, Wuhan, China
4 University of Pennsylvania school of design, Philadelphia, PA, 19104, United States
* Corresponding author: lianzhipeng1985@163.com
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.
Key words: Landslide susceptibility prediction / Frequency ratio / C5.0 decision tree / Geographical Information System
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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