Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy)

Abstract

Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km 2 in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area.

Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy

“…The area under the ROC curve can serve as a global accuracy statistic for the model. This statistic ranges from 0.5 (random prediction, represented by a diagonal straight line) to 1 (perfect prediction) and can be used for model comparisons (Cervi et al, 2010).…”

“…The susceptibility analysis of rainfall-induced shallow landslides on a large scale is usually performed using statistical methods (Carrara et al, 1991;Bai et al, 2009;Cervi et al, 2010;Li et al, 2012). More recently, physically based models proved rather promising in evaluating shallow landslide spatial susceptibility, starting from a distributed slopestability analysis (Montgomery and Dietrich, 1994;Wu and Sidle, 1995;Iverson, 2000;Qiu et al, 2007;Baum et al, 2008;Lu and Godt, 2008;Simoni et al, 2008;Baum and Godt, 2010).…”

Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

“…Such maps usually indicate the possible location of landslides on the basis of data regarding the events occurring in the past (Santacana et al, 2003). Many authors have applied statistical correlations, which are based on the knowledge of previous events and take into account, beside rainfalls, other important variables which can influence landslides, such as geology, geometry, groundwater and geotechnical characteristics of the soil (Carrara et al, 1991;Bai et al, 2009;Cervi et al, 2010).…”

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

“…The ROC graph consists of two axes: y-axis represents the sensitivity and x-axis represents the difference 1-specificity. Thus, high sensitivity indicates a high number of correct predictions, and high specificity (low 1−specificity) indicates a low number of incorrect predictions [53]. Among the statistics derived from ROC analysis, the area under the curve (AUC) value also plays a significant role.…”