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| Reference : Strategies to estimate ground susceptibility to landslide reactivation. A probabilistic ... |
| Scientific congresses and symposiums : Paper published in a book | |||
| Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography | |||
| http://hdl.handle.net/2268/5992 | |||
| Strategies to estimate ground susceptibility to landslide reactivation. A probabilistic application in W Belgium (Oudenaarde) | |
| English | |
Dewitte, Olivier  [Université de Liège - ULg > Département de géographie > Unité de géographie physique et quaternaire (UGPQ) >] | |
| Chung, Chang-Jo [Université de Liège - ULg > Département de géographie > Cartographie et systèmes d'information géographique >] | |
| Cornet, Yves [Université de Liège - ULg > Département de géographie > Unité de géographie physique et quaternaire (UGPQ) >] | |
| Demoulin, Alain [> > > >] | |
| 2007 | |
| S09-10 | |
| No | |
| International | |
| Society for Mathematical Geology XIth International Congress | |
| 3-8 septembre 2006 | |
| Université de Liège | |
| Liège | |
| Belgique | |
| [en] Landslide reactivation ; susceptibility ; probability ; fuzzy set membership function | |
| [en] In the hilly region of the Flemish Ardennes in western Belgium, no new big
deep-seated landslides have occurred for decades, whereas several reactivation episodes were recently observed in ancient landslides. We selected a test area comprised of 13 rotational landslides located close to the town of Oudenaarde in order to predict the susceptibility of their main scarp to retreat. We propose here two probabilistic models based on a fuzzy set approach. The models use empirical distribution functions (EDFs) as favourability values to build membership values and combine them by using the fuzzy Gamma operator. Based on Kolmogorov-Smirnov tests applied to these EDFs to select the most relevant data, a first model was obtained bases on a combination of 5 quantitative variables: slope angle, distance from cultivation located upstream of the main scarp, slope aspect, elevation and profile curvature. Another, more empirical approach based on the a posteriori analysis of the prediction-rate curves was applied to select the 4 variables of a second model: slope aspect, plan curvature, vegetation index and focal flow. According to the prediction-rate curves and the resulting susceptibility maps, the empirical model appears more efficient in locating the main scarp areas most prone to reactivation. | |
| Researchers | |
| http://hdl.handle.net/2268/5992 |
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