Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden

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Sökning: L773:1561 8633 OR L773:1684 9981 > (2015-2019) > Application of a fa...

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Fältnamn	Indikatorer	Metadata
000		03825naa a2200361 4500
001		oai:DiVA.org:uu-271074
003		SwePub
008		160105s2015 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-2710742 URI
024	7	a https://doi.org/10.5194/nhess-15-2703-20152 DOI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Melchiorre, Caterinau Uppsala universitet,Luft-, vatten- och landskapslära4 aut0 (Swepub:uu)catme122
245	1 0	a Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden
264		c 2015-12-21
264	1	b European Geosciences Union (EGU),c 2015
338		a electronic2 rdacarrier
520		a 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.
650	7	a NATURVETENSKAPx Geovetenskap och miljövetenskap0 (SwePub)1052 hsv//swe
650	7	a NATURAL SCIENCESx Earth and Related Environmental Sciences0 (SwePub)1052 hsv//eng
650	7	a NATURVETENSKAPx Geovetenskap och miljövetenskapx Multidisciplinär geovetenskap0 (SwePub)105032 hsv//swe
650	7	a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Geosciences, Multidisciplinary0 (SwePub)105032 hsv//eng
700	1	a Tryggvason, Ari,d 1967-u Uppsala universitet,Geofysik4 aut0 (Swepub:uu)aretrygg
710	2	a Uppsala universitetb Luft-, vatten- och landskapslära4 org
773	0	t Natural hazards and earth system sciencesd : European Geosciences Union (EGU)g 15:12, s. 2703-2713q 15:12<2703-2713x 1561-8633x 1684-9981
856	4	u https://doi.org/10.5194/nhess-15-2703-2015y Fulltext
856	4	u https://uu.diva-portal.org/smash/get/diva2:891120/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
856	4	u https://www.nat-hazards-earth-syst-sci.net/15/2703/2015/nhess-15-2703-2015.pdf
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-271074
856	4 8	u https://doi.org/10.5194/nhess-15-2703-2015

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