Assessment of shallow landslide susceptibility by means of multivariate statistical techniques

TLDR

The study uses multivariate statistical analyses to identify and quantify the most influential geological and geomorphological parameters driving shallow landslides. The authors prepared a dataset of over 30 attributes for 230 slopes, applied PCA, t‑tests, and one‑way tests to select key variables, used these in discriminant analysis, and generated a susceptibility map for the Ensija range. The discriminant function correctly classified 88.5 % of all slopes and 95.6 % of failures, with slope gradient, watershed area and land‑use as the strongest predictors, and the resulting density index corroborated the map. © 2001 John Wiley & Sons, Ltd.

Abstract

Abstract Several multivariate statistical analyses have been performed to identify the most influential geological and geomorphological parameters on shallow landsliding and to quantify their relative contribution. A data set was first prepared including more than 30 attributes of 230 failed and unfailed slopes. The performance of principal component analysis, t‐test and one‐way test, allowed a preliminary selection of the most significant variables, which were used as input variables for the discriminant analysis. The function obtained has classified successfully 88·5 per cent of the overall slope population and 95·6 per cent of the failed slopes. Slope gradient, watershed area and land‐use appeared as the most powerful discriminant factors. A landslide susceptibility map, based on the scores of the discriminant function, has been prepared for Ensija range in the Eastern Pyrenees. An index of relative landslide density shows that the results of the map are consistent. Copyright © 2001 John Wiley & Sons, Ltd.