Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)

Nefeslioğlu, Hakan A.; Duman, Tamer Y.; Durmaz, Serap

doi:10.1016/j.geomorph.2006.10.036

Cited by 261 publications

(97 citation statements)

References 35 publications

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“…A well-structured susceptibility evaluation procedure should consider the terrain conditions preceding the landslide events, since the failure occurrence could cause strong topographic modifications of these areas Fabbri, 1999, 2008;Fernandez et al, 2003;Ayalew and Yamagishi, 2005;Nefeslioglu et al, 2008;Clerici et al, 2010). To test the best feature to be used for each landslide type, our procedure provides for the application of the susceptibility analysis using, for landslide inventory, the depletion zones and the outer buffer areas from the depletion zones, the latter to be dimensioned as a function of the input data resolution.…”

Section: Methodological Backgroundmentioning

confidence: 99%

“…In heuristic (or index) methods, the causal factors are weighted subjectively (Hollingsworth and Kovacs, 1981;Bosi et al, 1985;Neeley and Rice, 1990;Montgomery et al, 1991;van Westen et al, 1999). Deterministic models are based on the physical laws driving landslides (Okimura and Kawatani, 1987;Hammond et al, 1992;Montgomery and Dietrich, 1994;Terlien et al, 1995;Pack et al, 1999;Iverson, 2000) and are generally more suitable for small areas or for slope-specific stability studies.…”

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

Vergari

Seta

Monte

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy), where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor maps. Finally, a validation procedure was applied based on the partition of the landslide inventory from multi-temporal aerial photo interpretation.Although encompassing some sources of uncertainties, the applied susceptibility assessment method provided a satisfactory and unbiased prediction for the Upper Orcia Valley. The results confirmed the efficiency of the selection procedure, as an unbiased step of the landslide susceptibility evaluation. Furthermore, we achieved the purpose of presenting a conceptually simple but, at the same time, effective statistical procedure for susceptibility analysis to be used as well by decision makers in land management.

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Section: Methodological Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

Vergari

Seta

Monte

et al. 2011

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Qualitative methods mainly refer to site analysis and overlay analysis of thematic maps (for example, geomorphological and geological characteristics) based on expert experience (Anbalagan and Singh 1996;Ayalew et al 2004). Quantitative methods mainly refer to various statistical analyses (Carrara et al 1991;Tang et al 2011a;Yoshimatsu and Abe 2006;Kamp et al 2008), such as artificial neural network (ANN) (Lee and Evangelista 2006;Yilmaz 2010), support vector machine (SVM) (Yao et al 2008;Yilmaz 2010;Xu et al 2012), Bayesian Network (Song et al 2012) and Logistic Regression (Dong et al 2011;Nefeslioglu et al 2006;Xu et al 2013a, b). These methods have different characteristics and adaptability for various environmental conditions of different regions (Tangestani 2009;Mohammady et al 2012;Ozdemir and Altural 2013).…”

Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent postearthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50°and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of 123Nat Hazards (2015) 75:2467-2487 DOI 10.1007/s11069-014-1441 landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

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“…Considering recent landslide literature, at this scale of analysis multivariate statistical techniques are the most commonly used tool in landslide susceptibility analysis (Yesilnacar and Topal, 2005;Nefeslioglu et al, 2008a;Nefeslioglu et al, 2008b).…”

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

“…geotechnical modelling) (e.g. Carrara et al, 1995;Soeters and Van Westen, 1996;Chung and Fabbri, 1999;Guzzetti et al, 1999;Nefeslioglu et al, 2008a;Nefeslioglu et al, 2008b;Van Westen et al, 2008). The statistical methods, which are often used at medium scale (1:25,000 to 1:50,000) are based on the analysis of the relationship between a set of geo-environmental factors that control slope instability, and an inventory of historical landslide occurrences Thiery et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Airborne laser scanning : for forested landslides investigation in temperate and tropical invironments

Razak¹

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Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)

Cited by 261 publications

References 35 publications

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Airborne laser scanning : for forested landslides investigation in temperate and tropical invironments

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