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Discussion on the tree-based machine learning model in the study of landslide susceptibility

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Abstract

This study reported an application of the tree-based models to landslide susceptibility. The landslide inventory and ten conditioning factors were first constructed, based on data availability and climate. Subsequently, three tree-based models, decision tree (DT), DT-Boosting, and random forest (RF), were established and compared with the support vector machine (SVM) to analyze the difference in model prediction. Finally, the effect and causes of tree-based algorithms on prediction results were explored based on the working mechanism of the susceptibility model. Results show that there is no multicollinearity among the conditioning factors. The predicted results produced by the tree-based model display the discontinuous distribution compared with the SVM, not only presented in the point-based prediction but the surface-based heterogeneity. Moreover, heterogeneity on the susceptibility map relates to the tree-based algorithm and factor grading, especially the classification of important factors. Besides, DT-Boosting appears the highest numerical features, with large values of AUC (0.981), specificity (0.960), sensitivity (0.956) and accuracy (0.958) in the training phase, and high prediction of AUC (0.862), specificity (0.759), sensitivity (0.843) and accuracy (0.801) in the validation phase. In terms of fluctuation, the RF is smaller than that of DT-Boosting. Further, the susceptibility map generated by RF, with the largest D-value of 7.81, can well capture the difference in landslide susceptibility. This study provides a deep understanding for the application of tree-based machine learning models to landslide susceptibility.

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Acknowledgements

This research is supported and funded by the National Key Research and Development Program of China (2016YFE0202400 and 2018YFC0809605). The authors are grateful for this support, and would like to thank the professors for providing the dates used in this study as well. Data related to this study have been uploaded, and the authors confirm that the data supporting the findings of this study are available within the supplementary materials.

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Funding was provided by “The National Key Research and Development Program of China” (Grant No. 2016YFE0202400 and 2018YFC0809605).

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Authors and Affiliations

  1. School of Civil Engineering, Harbin Institute of Technology, No. 73 Huanghe Rd., Nangang Dist., Harbin, 150001, China

    Qiang Liu, Aiping Tang & Ziyuan Huang

  2. Department of Engineering Management, Tianjin University, Tianjin, 300072, China

    Lixin Sun

  3. School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China

    Xiaosheng Han

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  1. Qiang Liu
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Liu, Q., Tang, A., Huang, Z. et al. Discussion on the tree-based machine learning model in the study of landslide susceptibility. Nat Hazards 113, 887–911 (2022). https://doi.org/10.1007/s11069-022-05329-4

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