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Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification

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Abstract

Landslides are one of the most devastating natural hazards worldwide. Landslides are triggered by different forces, such as earthquakes and typhoons, and have different characteristics in terms of distribution, influential factors, and process. The objectives of this study are to develop susceptibility maps using machine learning for two different triggering forces (earthquake and typhoon) and identify the main predisposing factors in mountainous regions of Pakistan and Taiwan. To compare different machine learning models for landslide susceptibility mapping, landslide susceptibility maps were developed using traditional (logistic regression) and modern techniques (decision tree). Results show that the spatial pattern of susceptibility map from logistic regression is continuously distributed, whereas that from the decision tree is crisp and sharp. From both models, consistent results show that the most important critical factors are completely different for both the earthquake- and typhoon-triggered landslides. For rainfall-triggered landslides in Taiwan, the most important factor of landslide susceptibility is the distance to the rivers, whereas, for earthquake-triggered landslides in Pakistan, the most important one is geological formations. Moreover, landslide susceptibility maps show that earthquake-triggered landslides tend to occur at the Muzaffarabad Formation, whereas rainstorm-induced landslides aggregate in the slope toe along the river.

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Acknowledgements

The authors would like to thank Mohammad Rohmaneo Darminto to validate the case study in Taiwan, and Dr. Muhammad Shafique (G-SAG) for providing us the validation data in Pakistan case study. We are grateful to the editors and anonymous reviewers for providing suggestions of paper improvement, and the MOST in Taiwan for financial supports.

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Authors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan

    Muhammad Zeeshan Ali & Hone-Jay Chu

  2. National Changhua University of Education, Changhua, Taiwan

    Yi-Chin Chen

  3. Institute of Space Technology, Islamabad, Pakistan

    Saleem Ullah

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  1. Muhammad Zeeshan Ali
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Correspondence to Hone-Jay Chu.

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Appendix

Appendix

See Figs. 11, 12, 13, 14.

Fig. 11
figure 11

Spatial distribution of influential factors for the Pakistan case a elevation b slope c aspect d curvature

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Fig. 12
figure 12

Spatial distribution of influential factors for the Pakistan case a distance to faults b geology c distance to rivers d wetness index

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Fig. 13
figure 13

Spatial distribution of influential factors for the Taiwan case a elevation b slope c aspect d curvature

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Fig. 14
figure 14

Spatial distribution of influential factors for the Taiwan case a distance to faults b geology c distance to rivers d wetness index

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Ali, M.Z., Chu, HJ., Chen, YC. et al. Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification. Environ Earth Sci 80, 233 (2021). https://doi.org/10.1007/s12665-021-09510-z

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