A procedure for landslide susceptibility zonation by the conditional analysis method

Clerici, A.; Perego, Susanna; Tellini, Claudio; Vescovi, Paolo

doi:10.1016/s0169-555x(02)00079-x

Cited by 281 publications

(165 citation statements)

References 4 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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“…There are several ways, from purely qualitative to quantitative, to produce predictive maps that portray the spatial distribution of landslide hazard (Guzzetti et al, 1999;Clerici et al, 2002;Chung and Fabbri, 2004). However, they all rely on the basic assumption that "slope failures in the future will be more likely to occur under the conditions which led to past and present instability" (Guzzetti et al, 1999, Chung and Fabbri, in press).…”

Section: Introductionmentioning

confidence: 99%

Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

Demoulin

Chung

2007

Geomorphology

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A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the "proximity to active fault" criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps.

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“…Su representación cartográfica y el análisis se llevaron a cabo e incorporaron en los SIG (Peark et al, 2005; Washington State Department of Natural Resources, 2006;Kalderon-Asael et al, 2008;Hervás y Bobrowsky, 2009;Guzzetti et al, 2009;Blahut et al, 2010;Wenkey et al, 2011). Por su parte, la evaluación de la susceptibilidad a deslizamientos y su zonificación se ha abordado de manera heurística, estadística o determinística (Dai et al, 2002;Clerici et al, 2002;Zhou et al, 2003;Metternicht et al, 2005;Demoulin y Chung, 2007;Castellanos-Abella y Van Westen, 2008). Asimismo se han hecho esfuerzos para probar y validar los modelos de susceptibilidad de deslizamientos de tierras (Montgomery y Dietrich, 1994;Pack et al, 1997Pack et al, , 1998Pack et al, , 2001Morrissey et al, 2001;Dietrich et al, 2001;Borga et al, 2002aBorga et al, , 2002bZaitchik et al, 2003;Wawer y Nowocień, 2003;Chinnayakanahalli et al, 2003;Lan et al, 2004;Winsemius et al, 2005;Legorreta-Paulín y Bursik, 2008).…”

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Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

Paulín

Bursik

Pouget

et al. 2014

BSGM

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Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos 343 ResumenEste artículo provee un panorama general de la investigación en curso del Instituto de Geografía de la UNAM, que busca crear una metodología integral para el desarrollo de cartografía de inventarios multitemporales, de susceptibilidad y estimación de volúmenes de deslizamientos mediante el uso de Sistemas de Información Geográfica (SIG). La metodología se establece para terrenos volcáni-cos, donde deslizamientos superficiales y flujos de escombros son frecuentes a lo largo de las laderas y los sistemas fluviales de los volcanes. La cuenca del Río Chiquito-Barranca del Muerto en el flanco suroeste del volcán Pico de Orizaba se ha seleccionado como caso de estudio para caracterizar las áreas que son propensas a la inestabilidad de las laderas en terrenos volcánicos. La ocurrencia de deslizamientos se determinó a través de un inventario de deslizamientos creado a partir de fotos aéreas multitemporales y trabajo de campo. En este estudio se modela (simula) la susceptibilidad a deslizamientos usando un modelo cartográfico-hidrológico (Stability Index MAPping (SINMAP)) y un modelo estadístico (Regresión Logística Múltiple (RLM)). Ambos modelos están integrados dentro de la aplicación LOGISNET (desarrollado mediante el uso de macros (AML) del sistema de información geográfica (SIG) ArcInfo Workstation. Su implementación mostró que ambos modelos poseen un grado moderado de concordancia con el inventario de deslizamientos, prefiriéndose la RLM para futuros análisis por su flexibilidad de incluir otras variables. Para la estimación de volúmenes de materiales aportados por los deslizamientos a los cauces, se seleccionaron los deslizamientos superficiales. El área y volumen de deslizamientos superficiales individuales fueron obtenidos en campo y a través de la base de datos del inventario de deslizamientos, para establecer una relación empírica: área-volumen. La relación se utilizó para el uso de una ley de potencias con la que se estimó el volumen total de deslizamientos de tierra en el área de estudio. Los resultados son importantes para comprender la evolución a largo plazo del sistema fluvial en el flanco suroeste del volcán Pico de Orizaba, y apoyar el desarrollo de una metodología útil para la elaboración de inventarios, el análisis de la susceptibilidad y estimación de volúmenes de deslizamientos en terrenos volcánicos.

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A procedure for landslide susceptibility zonation by the conditional analysis method

Cited by 281 publications

References 4 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

Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

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