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Landslides

, Volume 7, Issue 1, pp 55–68 | Cite as

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

  • Arzu Erener
  • H. Sebnem B. Düzgün
Original Paper

Abstract

Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.

Keywords

Logistic regression Spatial regression Geographically weighted regression Susceptibility mapping 

Notes

Acknowledgment

This study was conducted during the first author’s stay at ICG (International Centre for Geohazards) in Norway. The authors thank to ICG and NGI (Norwegian Geotechnical Institute) for providing financial support during the course of this research. The authors also acknowledge NGU (Geological Survey of Norway), Meteorological Institute of Norway for supporting this work with relevant background information and data, as well as Marc-Henri Derron for his comments on this work.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Geodetic and Geographic Information TechnologiesMiddle East Technical UniversityAnkaraTurkey

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