The present study discusses the use of integrated variables along with a combination of multi-method forecasts for landslide susceptibility mapping. The study area is located in the south-eastern French Massif central, a volcanic region containing Tertiary sedimentary materials that are prone to landslides. The flowage-type landslides within the study area are very slow-moving phenomena which affect the infrastructures and human settlements. The modelling process is based on a training set of landslides (70% of total landslides) and a set of controlling factor (slope, lithology, surficial formation, the topographic wetness index, the topographic position index, distance to thalweg, and aspect). We create a composite variable (or integrated variable), corresponding to the union of geology and surficial formation, in order to avoid the conditional dependence between these two variables and to build a geotechnical variable. We use five classical modelling methods (index, weight-of-evidence, logistic regression, decision tree, and unique condition unit) with the same training set but with different architectures of input data made up of controlling factors. All the models are tested with a validation group (30% of total landslides), using the Area Under the Receiver Operating Characteristic curve (AUC) to quantify their predictive performance. We finally select a single "best" model for each method. However, these five models are all equivalent in quality, despite their differences in detail, so no single model stands out against another. Finally, we combine the five models into a unique susceptibility map with a calculation of median susceptibility class. The final AUC value of this combined map is better than that for a single model (except for Unique Condition Unit), and we can evaluate the certainty of the susceptibility class pixel by pixel. In agreement with the sparse literature on this topic, we conclude that i) integrated variables increase the performance of classical modelling processes and ii) the combination of multi-method forecasts is a pragmatic solution to the inherent problem of choosing the most suitable method for the available data and geomorphological context. (C) 2014 Elsevier B.V. All rights reserved.
