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A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping

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Abstract

Landslides are among the most destructive natural hazards with severe socio-economic ramifications all around the world. Understanding the critical combination of geoenvironmental factors involved in the occurrence of landslides can mitigate the adverse impacts ascribed to them. Among the several scenarios for studying and investigating this phenomenon, landslide susceptibility mapping (LSM) is the most prominent method. Applying the machine learning (ML) algorithms integrated with the geographic information systems (GIS) has become a trending means for accurate and rapid landslide mapping practices in the scientific community. Support vector machine (SVM) has been the most commonly applied ML algorithm for LSM in recent years. The current study aims to implement different SVM kernel functions including polynomial kernel function (PKF) (degree 1 to 5), radial basis function (RBF), sigmoid, and linear kernels, for a GIS-based LSM over the Tabriz Basin (TB). To this end, a total number of 9 conditioning parameters being involved in the occurrence of the landslide events were determined and utilized. The LSM maps of the TB were generated based on the different SVM kernels and were statistically validated according to the landslide inventory. The findings revealed that the polynomial-degree-2 (PKF-2) model (AUC = 0.9688) outperforms the rest of the utilized kernels. According to the SLM map generated through PKF-2, the northernmost parts of the TB are extremely susceptible to slope failures than the rest; therefore, the developmental policies over these parts have to be taken into account with privileged priority to hinder any humanitarian as well as environmental catastrophes.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Khalil Valizadeh Kamran], upon reasonable request.

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Acknowledgements

The authors appreciate all the support they received from the research center of the University of Tabriz without which this research project will not be fulfilled successfully.

Funding

This research was fully funded by the research center Tabriz of University.

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Authors and Affiliations

  1. Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran

    Khalil Valizadeh Kamran, Bakhtiar Feizizadeh & Yousef Ebadi

  2. GISciences lab, Department of Geography, Humboldt University of Berlin, Berlin, Germany

    Bakhtiar Feizizadeh

  3. Department of GIS, Graduate School of Natural and Applied Sciences, Dokus Eylul University, Izmir, Turkey

    Behnam Khorrami

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  1. Khalil Valizadeh Kamran
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  2. Bakhtiar Feizizadeh
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  3. Behnam Khorrami
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Correspondence to Khalil Valizadeh Kamran.

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Kamran, K.V., Feizizadeh, B., Khorrami, B. et al. A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping. Appl Geomat 13, 837–851 (2021). https://doi.org/10.1007/s12518-021-00393-0

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