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

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.

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

“…In spite of the striking criticism which arises when selecting only a very limited subset of the mapped areas, a number of papers, which exploit logistic regression methods to produce grid cell susceptibility models, optimizes very sophisticated statistic procedures but disregards the real spatial representativeness of the fitted models; in these studies, models are trained solely on very limited part of the mapped basins, which typically stretch for hundreds of square kilometers, without verifying if changes in the random extraction of negative cases result in modifying the selected factors or their regression coefficients (Akgün 2012;Chauan et al 2010;Erener and Düzgün 2010;Mathew et al 2009;Nefeslioglu et al 2008;Ohlmacher and Davis 2003;Süzen and Doyuran 2004). At the same time, some other papers in literature deal with the estimation of robustness in terms of stability of the statistical procedure, disregarding the problem of the geologic representativeness of the subset on which regression is applied (e.g., Carrara et al 2008;Vorpahl et al 2012), using totally boot strapping-based procedures.…”

“…) Binary logistic regression (BLR) Atkinson and Massari (1998), Ayalew and Yamagishi (2005), Bai et al (2010), Can et al (2005), Carrara et al (2008), Chauan et al (2010), Conforti et al (2012), Dai and Lee (2002), Davis and Ohlmacher (2002), Erener and Düzgün (2010), Mathew et al (2009), Nandi and Shakoor (2009), Nefeslioglu et al (2008, Ohlmacher and Davis (2003), Van den Eckhaut et al (2006 Classification and regression trees (CART) Felicísimo et al (2012), Vorpahl et al (2012) Artificial neuronal networks (ANN) Aleotti and Chowdhury (1999), Ermini et al (2005), Lee et al (2004), Pradhan and Lee (2010) Original Paper exploited to compare the fitting of the model having only the constant term (all the β p are set to 0) with the fitting of the model that includes all the considered predictors with their estimated non-null coefficients so as to verify if the increase in likelihood is significant; in this case, at least one of the p coefficients is to be expected as different from zero (Hosmer and Lemeshow 2000). By exponentiating the β's, odds ratios (OR) for the independent variables are derived: these are measures of association between the independent variables and the outcome of the dependent, and directly express how much more likely (or unlikely) it is for the outcome to be positive (unstable cell) for unit changing of the considered independent variable.…”

Landslide Susceptibility Mapping of Canik (Samsun) District Using Bayesian Probability and Frequency Ratio Models

“…The altitude of the study area varies between 20 and 1000 m. The values of elevation were divided into ten categories with 100 m increments, and landslide-altitude relationship was identified. Using this map, it was identified that the most of the landslides with 36.31% frequency occurred in areas at altitudes ranging from 300 to 400 m in th e study area (Tables 2). Curvature shows the morphological structure of topography (Lee and Min, 2001;Erener and Düzgün, 2010). Curvature maps are obtained as second derivative of DEM, thus they show changes in the slope (Erener and Düzgün, 2010).…”

“…Using this map, it was identified that the most of the landslides with 36.31% frequency occurred in areas at altitudes ranging from 300 to 400 m in th e study area (Tables 2). Curvature shows the morphological structure of topography (Lee and Min, 2001;Erener and Düzgün, 2010). Curvature maps are obtained as second derivative of DEM, thus they show changes in the slope (Erener and Düzgün, 2010). A positive curvat ure indicates an upward convex surface, while a negative curvature is indicative of an upward concave surface, and zero represents a flat surface.…”

“…The frequency ratio model was used to determine the correlation between past landslide locations and each factor affecting landslides (Lee and Min, 2001;Lee et al, 2004;Erener and Düzgün, 2010). As defined by the Lee and Talib (2005), the frequency ratio is the "ratio of the area where landslides occurred in the total study area, and also, is the ratio of the probabilities of a landslide occurrence to a non-occurrence for a given attribute".…”