Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

S Pereira,J L Zêzere,C Bateira

doi:10.5194/nhess-12-979-2012

Abstract

Abstract. The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2) located in the Northern Portugal. In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use) and was compared with a model developed using all seven landslide predisposing factors. Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

Highlights

Recent developments in GIS software and increasing computing power allow a substantially high number of independent variables to be used in empirical, data-driven landslide susceptibility models
Information Values computed with the total set of landslides (Table 1) were integrated and 120 landslide susceptibility models were produced using all possible combinations of landslide predisposing factors (Table 2)
All possible combinations of up to seven variables related to landslide predisposing factors were modelled in this study, resulting in the quantitative comparison of 120 models

Summary

Introduction

Recent developments in GIS software and increasing computing power allow a substantially high number of independent variables to be used in empirical, data-driven landslide susceptibility models. Lee et al, 2002 (13 variables); Lee and Choi, 2004 (15 variables); The study area (Fig. 1) is Santa Marta de Penaguiao council (70 km2), located in the Northern Portugal. Recent studies in landslide susceptibility models usually involve over a dozen variables considered as predisposing factors of slope instability This area is part of the Iberian Hercynian Massif where plutonic and metamorphic rocks are dominant. Tectonic deformation explains the vigorous down cutting of the rivers, deep incised valleys and steep slopes.

Objectives

Results

Conclusion