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| Titel |
A strategy for GIS-based 3-D slope stability modelling over large areas |
| VerfasserIn |
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, F. Guzzetti |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 6 ; Nr. 7, no. 6 (2014-12-15), S.2969-2982 |
| Datensatznummer |
250115795
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| Publikation (Nr.) |
 copernicus.org/gmd-7-2969-2014.pdf |
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| Zusammenfassung |
| GIS-based deterministic models
may be used for landslide susceptibility mapping over large areas. However,
such efforts require specific strategies to (i) keep computing time at an
acceptable level, and (ii) parameterize the geotechnical data. We test and
optimize the performance of the GIS-based, 3-D slope stability model
r.slope.stability in terms of computing time and model results. The model was
developed as a C- and Python-based raster module of the open source software
GRASS GIS and considers the 3-D geometry of the sliding surface. It
calculates the factor of safety (FoS) and the probability of slope failure
(Pf) for a number of randomly selected potential slip surfaces,
ellipsoidal or truncated in shape. Model input consists of a digital elevation model (DEM), ranges of
geotechnical parameter values derived from laboratory tests, and a range of
possible soil depths estimated in the field. Probability density functions
are exploited to assign Pf to each ellipsoid. The model
calculates for each pixel multiple values of FoS and Pf
corresponding to different sliding surfaces. The minimum value of FoS and the
maximum value of Pf for each pixel give an estimate of the
landslide susceptibility in the study area. Optionally, r.slope.stability is
able to split the study area into a defined number of tiles, allowing
parallel processing of the model on the given area. Focusing on shallow
landslides, we show how multi-core processing makes it possible to reduce computing
times by a factor larger than 20 in the study area. We further demonstrate
how the number of random slip surfaces and the sampling of parameters
influence the average value of Pf and the capacity of
r.slope.stability to predict the observed patterns of shallow landslides in
the 89.5 km2 Collazzone area in Umbria, central Italy. |
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