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Application of novel ensemble models to improve landslide susceptibility mapping reliability

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Abstract

Most landslides in the Eastern Golestan province in Iran occur in the Doji watershed. Their number, however, lies at the lower limit for reliable statistical analyses. By selecting a statistical sample in an area with rather homogeneous conditions (thereby reducing the number of meaningful covariates), significant insights can nevertheless be obtained. We relied on an inventory of 145 landslides which discerns between types of movement and implemented six machine learning algorithms (Decorate, DE-REPTree, Random Subspace, RS-REPTree, Dagging, and DA-REPTree) to produce landslide susceptibility maps. This allowed us to evaluate the relative importance and the effect of covariates in the models and identify factors that are consistently associated with the presence of landslides. Our results demonstrate that, even for a small landslide inventory, reliable susceptibility maps can be produced for homogeneous landscapes. We discuss that our approach could be used to assess the reliability of statistical approaches at small scales, where a distinctive trigger is lacking.

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Authors and Affiliations

  1. School of Mechanical Skills Development Center, Changchun Sci-Tech University, Changchun, 130600, China

    Zhong ling Tong

  2. School of Vocational and Technical College, Changchun Sci-Tech University, Changchun, 130600, China

    Qing tao Guan

  3. Department of Geomorphology, Tarbiat Modares University, Tehran, 14117-13116, Iran

    Alireza Arabameri

  4. Institute of Hydrogeology, Engineering Geology and Applied Geophysics, Charles Universit, Albertov 6, 128 43, Prague, Czech Republic

    Marco Loche & Gianvito Scaringi

  5. Institute of Rock Structure & Mechanics, Czech Academy of Sciences, V Holešoviˇckách 41, 182 09, Prague, Czech Republic

    Marco Loche

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  1. Zhong ling Tong
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Appendix

Appendix

Table 5 Multicollinearity results for landslide conditioning factors. The variables were computed following the indication of several works (e.g., Heerdegen and Beran 1982; Forman and Godron 1986; Böhner and Selige 2006; Lombardo and Mai 2018)
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Table 6 Relationship between landslide conditioning factors and landslides using the frequency ratio model
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Table 7 Description of the lithology units in the study area
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Table 8 General properties of the landslide inventory
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Table 9 Percentage values of each susceptibility class and ASCII values for each model
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Fig. 6
figure 6

Landslide conditioning factors

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Fig. 7
figure 7

Flowchart of research in the study area

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Fig. 8
figure 8

Some examples of landslides in the study area

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Tong, Z.l., Guan, Q.t., Arabameri, A. et al. Application of novel ensemble models to improve landslide susceptibility mapping reliability. Bull Eng Geol Environ 82, 309 (2023). https://doi.org/10.1007/s10064-023-03328-8

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