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Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction

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Abstract

Landslide susceptibility prediction is a key step in preventing and managing landslide hazards. As a classical supervised non-parametric machine learning model, support vector machine (SVM) has been widely used in landslide susceptibility prediction in recent years. However, most studies focus on the application of general SVM methods, or compare SVMs as benchmark methods. SVMs with different kernel functions are rarely used in this field. In this study, we apply the general SVM and its popular variants (i.e., multiple kernel learning, infinite ensemble SVM and semi-supervised SVM) to predict landslide susceptibility, and compare their prediction performance. The experimental results show that the Laplacian-SVM has the highest prediction performance (AUC = 0.8815) among SVM-based methods. SVMs with RBF kernel can achieve higher performance than SVMs with linear kernel, indicating that RBF kernel is more suitable for solving susceptibility prediction problems. Furthermore, SVM-based methods have higher sensitivity (0.8543–0.9288) than deep learning methods (0.8237–0.8271), which proves the advantage of SVMs in finding potential landslide areas.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

AveMKL:

Average multiple kernel learning

CNN:

Convolutional neural network

CS4VM:

Cost sensitive semi-supervised support vector machine

CSLapSVM:

Cost sensitive laplacian semi-supervised support vector machine

DNN:

Deep neural network

IE-SVM:

Infinite ensemble support vector machine

L1MKL:

Multiple kernel learning with one-norm

L2MKL:

Multiple kernel learning with two-norm

LpMKL:

Multiple kernel learning with p-norm

LN-SVM:

Support vector machine with linear kernel

LSP:

Landslide susceptibility prediction

MeanS3VM:

Label mean semi-supervised support vector machine

MK-SVM:

Multiple kernel support vector machine

PL-SVM:

Support vector machine with polynomial kernel

RBF-SVM:

Support vector machine with radial basis function kernel

SIG-SVM:

Support vector machine with sigmoid kernel

SimpleMKL:

Simple multiple kernel learning

SS-SVM:

Semi-supervised support vector machine

SVM:

Support vector machine

TPI:

Terrain position index

TRI:

Terrain ruggedness index

TSC:

Terrain surface convexity

TST:

Terrain surface texture

TSVM:

Transductive semi-supervised support vector machine

TWI:

Topographic wetness index

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Acknowledgements

We are grateful to the Headquarters of Prevention and Control of Geo-Hazards in Area of Three Gorges Reservoir for providing data and material. We would also like to thank the handling editors and four anonymous reviewers for their valuable comments and suggestions, which significantly improved the quality of this paper.

Funding

This work was supported by the Joint Funds of the National Natural Science Foundation of China (U21A2013), the State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences (GBL12107), the National Natural Science Foundation of China (61271408, 41602362), and the Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan).

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  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Zhice Fang, Yi Wang, Hexiang Duan & Ruiqing Niu

  2. China Institute of Geo-Environment Monitoring, Beijing, 100081, China

    Ling Peng

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  1. Zhice Fang
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ZF Data curation, Methodology, Validation, Visualization, Roles/Writing—original draft. YW Conceptualization, Formal analysis, Supervision, Writing—review & editing, Funding acquisition. HD Investigation; Writing—review & editing. RN Writing—review & editing. Ling Peng: Writing—review & editing, Funding acquisition.

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Table 10 Parameters of SVM-based methods
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Fang, Z., Wang, Y., Duan, H. et al. Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction. Stoch Environ Res Risk Assess 36, 3535–3556 (2022). https://doi.org/10.1007/s00477-022-02208-z

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