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| Titel |
Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden |
| VerfasserIn |
C. Melchiorre, A. Tryggvason |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 15, no. 12 ; Nr. 15, no. 12 (2015-12-21), S.2703-2713 |
| Datensatznummer |
250119823
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| Publikation (Nr.) |
 copernicus.org/nhess-15-2703-2015.pdf |
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| Zusammenfassung |
| We refine and test an algorithm for landslide susceptibility assessment in
areas with sensitive clays. The algorithm uses soil data and digital
elevation models to identify areas which may be prone to landslides and has
been applied in Sweden for several years. The algorithm is very
computationally efficient and includes an intelligent filtering procedure for
identifying and removing small-scale artifacts in the hazard maps produced.
Where information on bedrock depth is available, this can be included in the
analysis, as can information on several soil-type-based cross-sectional angle
thresholds for slip. We evaluate how processing choices such as of filtering
parameters, local cross-sectional angle thresholds, and inclusion of bedrock
depth information affect model performance. The specific cross-sectional
angle thresholds used were derived by analyzing the relationship between
landslide scarps and the quick-clay susceptibility index (QCSI). We tested
the algorithm in the Göta River valley. Several different verification
measures were used to compare results with observed landslides and thereby
identify the optimal algorithm parameters. Our results show that even though
a relationship between the cross-sectional angle threshold and the QCSI could
be established, no significant improvement of the overall modeling
performance could be achieved by using these geographically specific,
soil-based thresholds. Our results indicate that lowering the cross-sectional
angle threshold from 1 : 10 (the general value used in Sweden) to 1 : 13
improves results slightly. We also show that an application of the automatic
filtering procedure that removes areas initially classified as prone to
landslides not only removes artifacts and makes the maps visually more
appealing, but it also improves the model performance. |
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