Evaluation of landslide susceptibility using multivariate statistical methods: a case study in the Prahova subcarpathians, Romania

The shallow landslides affect most of the hillslopes in the Prahova Subcarpathians and are mainly caused by the agriculture practices. Though, these processes do not imply immediately losses for human activity, the landslide susceptibility assessment is necessary to prevent terrain degradation. The evaluation of landslide susceptibility requires understanding of spatial distribution of the factors that control slope instability. It is known that the behavior of landslides is difficult to evaluate because of the various factors that trigger the mass movements. One of the most efficient methods to establish the importance of the factors in landslides susceptibility is the multivariate analysis, like Principal Component Analysis (PCA) and logistic regression. For this study, the PCA was applied to remove the redundant information from the elements that control slope instability like lithology, faults, slope gradient, plan curvature, profile curvature, land use and land cover, soils etc. The PCA analysis had reduced the factors at six principal components and the first three components comprise over 85% of the information. The logistic regression analysis was used to correlate the presence of the past and actual landslides with the PCA components and derive a landslides susceptibility map. To validate the model we used the Receiver Operating Characteristic (ROC) analysis. ROC shows a good correlation between the observed and predicted values of the validation dataset.