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The performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery

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A Correction to this article was published on 24 February 2021

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Abstract

An earthquake with Ms 7.0 (33.2° N, 103.8° E) occurred in Jiuzhaigou County of Sichuan Province in China on 8 August 2017. This earthquake triggered a large number of landslides in the study area. Although the susceptibility quality level index has improved, the high-quality assessments still have remained rare. We adopted three models, including the logistic regression (LR), support vector machine (SVM), and random forest (RF) to study the quality performance of the susceptibility distribution rule of earthquakes induced landslides. We used satellite images of before and after earthquakes and landslides as well. We used the area under receiver operating characteristic (ROC) curve (AUC) and ratio to evaluate the model’s accuracy and quality performance, including the mapping availability susceptibility assessment. This study reveals that RF has the highest ratio (2.07) as compared to the LR (1.78) and SVM (1.90). The result shows that RF has more potential to implement future experiments in Sichuan Province because of a better performance quality in the susceptibility assessment of landslides induced by earthquakes.

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Acknowledgements

The authors like to express appreciation to the lab’s staff for their valuable comments and contributions. We also appreciate Professor Jonathan Li, from the University of Waterloo, Canada, for his support during the research.

Funding

The funding of this study is from the Foundation of Sichuan Educational Committee (No.14ZB0071). We also received funds from the Science and Technology Department of the Sichuan Province Technology Support Program (No.2012FZ0018).

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Authors and Affiliations

  1. College of Geophysics, Chengdu University of Technology, Sichuan, 610059, China

    Rui Liu, Luyao Li & Xin Yang

  2. Key Lab of Earth Exploration and Information Techniques of Ministry Education, Chengdu University of Technology, Sichuan, 610059, China

    Rui Liu

  3. Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, The Western Park of the Hi-Tech Industrial Development Zone, Chengdu, 611756, Sichuan, China

    Saied Pirasteh

  4. Dongpo District, Meishan, Sichuan Province, Housing and Urban-Rural Construction Bureau, Meishan, 620000, Sichuan, China

    Zili Lai

  5. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  6. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

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  1. Rui Liu
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Correspondence to Saied Pirasteh.

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Responsible Editor: Biswajeet Pradhan

The original online version of this article was revised: The corresponding author missed the affiliation of the sixth co-author, Himan Shahabi, and was incorrectly presented as “Southwest Jiaotong University”. The correct sixth co-author affiliation is “Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran and Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran. Given in this article is the corrected affiliation of sixth co-author.

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Liu, R., Li, L., Pirasteh, S. et al. The performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery. Arab J Geosci 14, 259 (2021). https://doi.org/10.1007/s12517-021-06573-x

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