E3S Web Conf.
Volume 198, 20202020 10th Chinese Geosynthetics Conference & International Symposium on Civil Engineering and Geosynthetics (ISCEG 2020)
|Number of page(s)||4|
|Section||Exploration and Innovation of Construction Engineering Technology|
|Published online||26 October 2020|
Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China
1 College of Geophysics, Chengdu University of Technology, Sichuan, 610059, China
2 Key Lab of Earth Exploration and Information technique of Ministry Education, Chengdu University of Technology, Sichuan, 610059, China
* Corresponding author: firstname.lastname@example.org
Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.
© The Authors, published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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