Multivariate models for landslide hazard evaluation

Carrara, A.

doi:10.1007/bf01031290

Cited by 436 publications

(255 citation statements)

References 9 publications

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“…From multivariate analysis, better hypothetical correlation between instability factors and landslide occurrence is perceived and it provides more reliable response (Carrara, 1983;Carrara et al, 1991Carrara et al, , 1992Mark & Ellen, 1995;Clerici et al, 2002;Ayalew & Yamagishi, 2004). Multivariate regression analysis plays a central role in statistics that cause one of the most powerful and commonly used techniques.…”

Section: )mentioning

confidence: 99%

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Mashari¹,

Solaimani

Omidvar

2012

ENRR

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Landslide is a natural hazard that causes many damages to the environment. Depending on the landform, several factors can cause the Landslide. This research addresses the methodology for landslide susceptibility mapping using multiple regression analysis and GIS tools. Based on the initial hypothesis, ten factors were recognized as effectual elements on landslide, which is geology, slope, aspect, distance from roads, faults and drainage network, soil capability, land use and rainfall. Crossing investigated parameters with the observed landslides indicated that three factor including distance from channel network, distance from fault and rainfall have no major effect on observed landslide in Tajan area. In order to quantifying the parameters in the form of weighting factors, the coverage of landslides in different observation was determined. Then Stepwise method was used for statistical analysis. It was found that slope, aspect, distance from the roads and soil capability are as most effective factors in landslide respectively.

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Section: )mentioning

confidence: 99%

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Mashari¹,

Solaimani

Omidvar

2012

ENRR

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“…Baeza and Corominas 2001;Beguerıa 2006;Piacentini et al 2012;Galve et al 2015;Trigila et al 2015;Persichillo et al 2016). These methods are based on conceptual models that describe the functional relationships between instability factors and the past and present distribution of slope failures (Carrara 1983). Bivariate and multivariate approaches are the most common statistical techniques, though probabilistic (e.g.…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

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“…In the past, DTM-based identification of PRA for different types of mass movements have been developed, in particular for shallow and deep-seated landslides (Carrara, 1983;Carrara and Guzetti, 1995;Singh et al, 2005;Gruber et al, 2009;Pradhan and Buchroithner, 2012) as well as for debris flows and rock falls (Singh et al, 2005;Michoud et al, 2012). The most important DTM-derived parameter for landslides and rock falls is the slope angle, which strongly determines the distribution of unstable areas (Carrara and Guzetti, 1995).…”

Section: Existing Algorithmsmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Preprint

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Abstract. Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were 10 developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such areawide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial 15 planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-theart numerical avalanche simulations.In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, 20 that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-25 based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 -30 years return period) and extreme (100 -300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and 30 snow data are available.Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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Multivariate models for landslide hazard evaluation

Cited by 436 publications

References 9 publications

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

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