Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City

Li He (), Xiantan Wu, Zhengwei He, Dongjian Xue, Fang Luo, Wenqian Bai, Guichuan Kang, Xin Chen and Yuxiang Zhang
Additional contact information
Li He: Key Laboratory of the Northern Qinghai–Tibet Plateau Geological Processes and Mineral Resources, Xining 810000, China
Xiantan Wu: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Zhengwei He: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Dongjian Xue: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Fang Luo: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Wenqian Bai: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Guichuan Kang: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Xin Chen: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Yuxiang Zhang: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2023, vol. 15, issue 20, 1-18

Abstract: Landslide susceptibility assessment can effectively predict the spatial distribution of potential landslides, which is of great significance in fields such as geological disaster prevention, urban planning, etc. Taking Xining City as an example, based on GF-2 remote sensing image data and combined with field survey data, this study delineated the spatial distribution range of developed landslides. Key factors controlling landslides were then extracted to establish a landslide susceptibility assessment index system. Based on this, the frequency ratio (FR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models were applied to spatially predict landslide susceptibility with slope units as the basis. The main results are as follows: (1) The overall spatial distribution of landslide susceptibility classes in Xining City is consistent, but the differences between different landslide susceptibility classes are significant. (2) The high-susceptibility area predicted by the FR-RF model is the largest, accounting for 15.48% of the total study area. The prediction results of the FR-ANN and FR-SVM models are more similar, with high-susceptibility areas accounting for 13.96% and 12.97%, respectively. (3) The accuracy verification results show that all three coupled models have good spatial prediction capabilities in the study area. The order of landslide susceptibility prediction capabilities from high to low is FR-RF model > FR-ANN model > FR-SVM model. This indicates that in the study area, the FR-RF model is more suitable for carrying out landslide susceptibility assessment.

Keywords: landslide susceptibility; machine learning; Xining City; loess area (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/20/14761/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/20/14761/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:20:p:14761-:d:1257773

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().