A systematic review of landslide probability mapping using logistic regression

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.

Appendices

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

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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.

Lee, S., (2005a), ‘Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data’, International Journal of Remote Sensing, 26, 7, 1477–1491.

Lee, S., (2005b), ‘Application and cross-validation of spatial logistic multiple regression for landslide susceptibility analysis’, Geosciences Journal, 9, 1, 63–71.

Lee, S., (2007), ‘Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea’, Earth Surface Processes and Landforms, 32, 2133–2148.

Lepore, C., Kamal, S.A., Shanahan, P., and Bras, R.L., (2012), ‘Rainfall-induced landslide susceptibility zonation of Puerto Rico’, Environmental Earth Sciences, 66, 1667–1681.

Li, X.P., and Zhou, S.P., (2012), ‘Application and research of GIS-based Wushan County slope stability evaluation information system’, Procedia Engineering, 29, 2296–2302.

Mancini, F., Ceppi, C., and Ritrovato, G., (2010), ‘GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy’, Natural Hazards and Earth System Sciences, 10, 1851–1864.

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Mathew, J., Jha, V.K., and Rawat, G.S., (2007), ‘Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India’, International Journal of Remote Sensing, 28, 10, 2257–2275.

Menendez-Duarte, R., Marquinez, J., and Devoli, G., (2003), ‘Slope instability in Nicaragua triggered by Hurricane Mitch: distribution of shallow mass movements’, Environmental Geology, 44, 290–300.

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Ohlmacher, G.C., and Davis, J.C., (2003), ‘Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA’, Engineering Geology, 69, 331–343.

Ozdemir, A., and Altural, T., (2013), ‘A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey’, Journal of Asian Earth Sciences, 64, 180–197.

Park, S., Choi, C., Kim, B., and Kim, J., (2013), ‘Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea’, Environmental Earth Sciences, 68, 1443–1464.

Pradhan, B., and Youssef, A.M., (2010), ‘Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models’, Arab Journal of Geosciences, 3, 319–326.

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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