Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G. and Vandekerckhove, L. (2006) Prediction of Landslide Susceptibility Using Rare Events Logistic Regression: A Case-Study in the Flemish Ardennes (Belgium). Geomorphology, 76, 392-410.
https://doi.org/10.1016/j.geomorph.2005.12.003

has been cited by the following article:

• TITLE: Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy)

  AUTHORS: Vittorio Chiessi, Simona Toti, Valerio Vitale

  KEYWORDS: Landslide Susceptibility, Antrodoco, Conditional Analysis, Rare Events Logistic Regression, Classification Methods

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.4 No.12, November 30, 2016

  ABSTRACT: This paper discusses some methodological aspects for the production of susceptibility maps of slope instability developed within the CARG Project (Geological Cartography of Italy at 1:50,000 scale). It describes an example of a susceptibility map in the presence of low susceptibility, using database having zero or negligible cost, with the aim to test some methodologies that can be easily reproducible to get a first estimate of the landslide susceptibility on a wide area. Two statistical approaches have been applied: the non-parametric conditional analysis and the logistic analysis for rare events. The predictive ability obtained from the two methodologies, was evaluated by the success-prediction curves for the conditional analysis, and by the Receiver Operating Characteristic curve (ROC), for the logistic model. The landslide susceptibility maps have been classified into four classes using both the Natural Breaks algorithm and the method proposed by Chung and Fabbri (2003). The paper considers the influence of these two classification methods on the quality of final results.