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A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas

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Abstract

Landslide is regarded as one of the most prevalent and destroying geological hazards in natural terrain areas. Reliable landslide susceptibility analysis procedures are vital for policymakers to manage the regional-scale landslide risk. In the framework of physically based modelling analysis, the infinite slope model is commonly used to assess the surficial landslide susceptibility with deterministically defined geotechnical and geological parameters. This work aims to develop a user-friendly geographic information system (GIS) extension tool called the GIS-FORM landslide prediction toolbox using the Python programming language to consider the possible uncertainties in the physically based landslide susceptibility analysis in seismic areas. We implement the first-order reliability method (FORM) algorithm to calculate the probability of infinite slope failures. The proposed toolbox can produce some regional hazard distribution maps of different indexes, such as the factor of safety (FoS), reliability index (RI), and failure probability (Pf). Furthermore, the toolbox enables coseismic landslide displacement prediction using either the direct Newmark integration method and/or the empirical formula method. Outputs of the GIS-FORM landslide prediction analysis are verified using published data in the literature. Further, it is also successfully employed for landslide susceptibility analysis of the Ms 7.0 Jiuzhaigou earthquake in Sichuan Province, China. Without loss of generality, the GIS-FORM landslide prediction toolbox can serve for the rapid hazard mapping of earthquake-induced regional landslides where uncertainties in geological and geotechnical parameters should be considered.

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Availability of data and material

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

Software application.

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Acknowledgements

We would like to declare that the ArcPy toolbox of GIS-TISSA provided as a .pyt file as well as hosted at the web link, https://pages.mtu.edu/~toommen/GeoHazard.html, inspired this study in many ways. Some mapping software, such as the ArcGIS and Python, are used.

Funding

This work is financially supported by the National Natural Science Foundation of China (NSFC GRANT NOS: 51879091, 52079045, 52178325).

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Authors and Affiliations

  1. Geotechnical Research Institute, Hohai University, Nanjing, China

    Jian Ji, Hongzhi Cui, Tong Zhang, Jian Song & Yufeng Gao

  2. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, China

    Jian Ji, Jian Song & Yufeng Gao

Authors
  1. Jian Ji
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  2. Hongzhi Cui
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  3. Tong Zhang
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  4. Jian Song
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  5. Yufeng Gao
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Contributions

Jian Ji: conceptualization, methodology, writing–review and editing, supervision. Hongzhi Cui: conceptualization, methodology, investigation, writing–original draft, writing–review and editing. Tong Zhang: software, data curation. Jian Song: methodology, writing–review. Yufeng Gao: conceptualization, methodology–review.

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Correspondence to Jian Ji.

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Ji, J., Cui, H., Zhang, T. et al. A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas. Landslides 19, 2213–2231 (2022). https://doi.org/10.1007/s10346-022-01885-9

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