Multivariate Regression Analysis for Landslide Hazard Zonation

Chung, Chae-Heon; Fabbri, Andrea G.; Westen, C.J. van

doi:10.1007/978-94-015-8404-3_7

Cited by 231 publications

(123 citation statements)

References 10 publications

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“…These map units conceptually correspond to the Unique Condition Units (UCU, Carrara et al, 1995;Chung et al, 1995), but formally differ from them because in this study they come from vector datasets so that we called them vUCUs (vector Unique Condition Units). Moreover, the vUCUs are different for each landslide type susceptibility analysis, as a consequence of the application of the factor selection procedure.…”

Section: Methodological Backgroundmentioning

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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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%

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

“…Such a statistics-oriented approach finally leads to fully quantitative maps that locate future landslide occurrence as a result of a GIS-based multivariate statistical analysis (Rezig et al, 1996;Leroi, 1997). Provided a validation of the results is carried out, these are objective and meaningful tools for landslide hazard prediction (Chung et al, 1995;Chung and Fabbri, 2004, 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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“…Little work was done on rock-fall susceptibility mapping based on GIS (e.g., Carrara et al 1995;Chung et al 1995;Guzzetti et al 1999;Suzen and Doyuran 2004a, b;Chau et al 2004). Susceptibility maps are found to be very useful in estimating, managing, and mitigating mass movement susceptibility for a region (e.g., Corominas and Santacana 2003;Chung and Fabbri 2003;Sassa et al 2004).…”

mentioning

confidence: 99%

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

et al. 2012

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This study describes the application of logistic regression to rock-fall susceptibility mapping along 11 km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76 % of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57 %. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall.

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Multivariate Regression Analysis for Landslide Hazard Zonation

Cited by 231 publications

References 10 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

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

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

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