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A systematic review of landslide probability mapping using logistic regression

  • Review Article
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Abstract

Logistic regression studies which assess landslide susceptibility are widely available in the literature. However, a global review of these studies to synthesise and compare the results does not exist. There are currently no guidelines for the selection of covariates to be used in logistic regression analysis, and as such, the covariates selected vary widely between studies. An inventory of significant covariates associated with landsliding produced from the full set of such studies globally would be a useful aid to the selection of covariates in future logistic regression studies. Thus, studies using logistic regression for landslide susceptibility estimation published in the literature were collated, and a database was created of the significant factors affecting the generation of landslides. The database records the paper the data were taken from, the year of publication, the approximate longitude and latitude of the study area, the trigger method (where appropriate) and the most dominant type of landslides occurring in the study area. The significant and non-significant (at the 95 % confidence level) covariates were recorded, as well as their coefficient, statistical significance and unit of measurement. The most common statistically significant covariate used in landslide logistic regression was slope, followed by aspect. The significant covariates related to landsliding varied for earthquake-induced landslides compared to rainfall-induced landslides, and between landslide type. More importantly, the full range of covariates used was identified along with their frequencies of inclusion. The analysis showed that there needs to be more clarity and consistency in the methodology for selecting covariates for logistic regression analysis and in the metrics included when presenting the results. Several recommendations for future studies were given.

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Acknowledgments

We would like to acknowledge all authors mentioned in the Appendix 1 reference list for their publications of logistic regression analysis of landslide susceptibility and hazard.

Author information

Authors and Affiliations

  1. Faculty of Social and Human Sciences, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

    M. E. A. Budimir & P. M. Atkinson

  2. Faculty of Engineering and the Environment, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

    H. G. Lewis

Authors
  1. M. E. A. Budimir
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  2. P. M. Atkinson
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Corresponding author

Correspondence to M. E. A. Budimir.

Appendices

Appendix 1 List of papers accepted from the systematic literature search for analysis in this paper

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Akgun, A., (2012), ‘A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey’, Landslides, 9, 93–106.

Atkinson, P.M., and Massari, R., (2011), ‘Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy’, Geomorphology, 130, 55–64.

Ayalew, L., and Yamagashi, H., (2005), ‘The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan’, Geomorphology, 65, 15–31.

Ayalew, L., Yamagashi, H., Marui, H., and Kanno, T., (2005), ‘Landslides in Sado Island of Japan: part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications’, Engineering Geology, 81, 432–445.

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Bai, S.B., Wang, J., Lu, G.N., Zhou, P.G., Hou, S.S., and Xu, S.N., (2010), ‘GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China’, Geomorphology, 115, 23–31.

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Dominguez-Cuesta, M.J., Jimenez-Sanchez, M., Colubi, A., and Gonzalez-Rodriguez, G., (2010), ‘Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment’, International Journal of Earth Science, 99, 661–674.

Duman, T.Y., Can, T., Gokceoglu, C., Nefeslioglu, H.A., and Sonmez, H., (2006), ‘Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey’, Environmental Geology, 51, 241–256.

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Falaschi, F., Giacomelli, F., Federici, P.R., Pucinelli, A., D’Amato Avanzi, G., Pchini, A., and Ribolini, A., (2009), ‘Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy’, Natural Hazards, 50, 551–569.

Federici, P.R., Puccinelli, A., Cantarelli, E., Casarosa, N., Avanzi, G., Falaschi, F., Giannecchini, R., Pochini, A., Ribolini, A., Bottai, M., Salvati, N., and Testi, C., (2007), ‘Multidisciplinary investigations in evaluating landslide susceptibility—an example in the Serchio River valley (Italy)’, Quaternary International, 171–172, 52–63.

Fenghuan, S., Peng, C., Jianqiang, Z., and Lingzhi, X., (2010), ‘Susceptibility assessment of landslides caused by the Wenchaun earthquake using a logistic regression model’, Journal of Mountain Science, 7, 234–245.

Garcia-Rodriguez, M.J., Malpica, J.A., Benito, B., and Diaz, M., (2008), ‘Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression’, Geomorphology, 95, 172–191.

Ghosh, S., Carranza, E.J.M., van Westen, C.J., Jetten, V.G., and Bhattacharya, D.N., (2011), ‘Selecting and weighting spatial predictors for empirical modelling of landslide susceptibility in the Darjeeling Himalayas (India)’, Geomorphology, 131, 35–56.

Greco, R., Sorriso-Valvo, M., and Catalano, E., (2007), ‘Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy’, Engineering Geology, 89, 47–66.

Guns, M., and Vanacker, V., (2012), ‘Logistic regression applied to natural hazards: rare event logistic regression with replications’, Natural Hazards and Earth System Sciences, 12, 1937–1947.

Hadji, R., Boumazbeur, A., Limani, Y., Bagham, M., el Madjid Chouabi, A., and Demdoum, A., (2013), ‘Geologic, topographic and climatic controls in landslide hazard assessment using GIS modelling: a case study of Souk Ahras region, NE Algeria’, Quaternary International, 302, 224–237.

Jaiswal, P., van Westen, C.J., and Jetten, V., (2010), ‘Quantitative landslide hazard assessment along a transportation corridor in southern India’, Engineering Geology, 116, 236–250.

Kincal, C., Akgun, A., and Koca, M.Y., (2009), ‘Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method’, Environmental Earth Science, 59, 745–756.

Knapen, A., Kitutu, M.G., Poesen, J., Breugelmans, W., Deckers, J., and Muwanga, A., (2006), ‘Landslide in a densely populated county at the footslopes of Mount Elgon (Uganda): characteristics and causal factors’, Geomorphology, 73, 149–165.

Lee, S., and Pradhan, B., (2007), ‘Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models’, Landslides, 4, 33–41.

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Lee, S., Ryu, J.H., and Kim, I.S., (2007), ‘Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea’, Landslides, 4, 327–338.

Lee, S.T., Yu, T.T. Peng, W.F., and Wang, C.L., (2010), ‘Incorporating the effects of topographic amplification in the analysis of earthquake-induced landslide hazards using logistic regression’, Natural Hazards and Earth System Sciences, 10, 2475–2488.

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Appendix 2 Covariates assigned to the ‘Other’ label in the systematic literature search

Bedrock depth

Bedrock-slope relationship

Convergence index

Crown density

Debris

Distance to drainage2

Distance to path

Distance to residential area

Elevation2

Exposition

Forest age

Forest degradation

Forest density

Forest diameter

Groundwater depth

Kinematic depth

Liquidity index

(Marly limestone) × (log of slope angle)

Mean watershed angle

Potential radiation

Proximity to old rock slide

Regolith thickness

Relative permeability

Strata orientation

Tectonic uplift

Tree age

Tree diameter

Wood age

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Budimir, M.E.A., Atkinson, P.M. & Lewis, H.G. A systematic review of landslide probability mapping using logistic regression. Landslides 12, 419–436 (2015). https://doi.org/10.1007/s10346-014-0550-5

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