Validation of Spatial Prediction Models for Landslide Hazard Mapping

Chung, Chae-Heon; Fabbri, Andrea G.

doi:10.1023/b:nhaz.0000007172.62651.2b

Cited by 1,061 publications

(628 citation statements)

References 5 publications

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“…The first landslide inventory was obtained using the directly available BDMvt (French geological survey landslide database, Couëffé et al, 2005). This is a free database that can be directly downloaded from the BRGM website.…”

Section: Landslide Datamentioning

confidence: 99%

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Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Fressard

Thiéry²,

Maquaire

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper aims at assessing the impact of the data set quality for landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted on the Pays d'Auge plateau (Normandy, France) with a scale objective of 1 / 10 000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, and geomorphological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlight that only high-quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formation maps) can predict a satisfying proportion of landslides in the study area.

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Section: Landslide Datamentioning

confidence: 99%

“…This is a free database that can be directly downloaded from the BRGM website. It was compiled using the landsliderelated incident records, and questionnaires sent to city councils (Couëffé et al, 2005). Only landslides referenced with a high spatial accuracy were used for the analysis.…”

Section: Landslide Datamentioning

confidence: 99%

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Fressard

Thiéry²,

Maquaire

2014

Nat. Hazards Earth Syst. Sci.

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“…Four susceptibility classes were defined for each susceptibility model according to the same Information Value scores (Ij) to enable The Effectiveness Ratio proposed by Chung and Fabbri (2003) was used to assess the performance and predictive power of susceptibility models by comparing the proportion of unstabilized area in each susceptibility class (AL) with a proportion of the susceptibility class in the entire study area (AS). With these terms, the ratio AL/AS is used to define "effective" and "very effective" susceptibility classes.…”

Section: Landslide Susceptibility Modelsmentioning

confidence: 99%

“…With these terms, the ratio AL/AS is used to define "effective" and "very effective" susceptibility classes. To be considered a powerful and effective prediction class, the ratio should be near zero in classes of low susceptibility and very large in classes of high and very high susceptibilities (Chung and Fabbri, 2003). This method has been developed for matrix models, for which it was considered that the ratio of "effective" susceptibility classes should be either higher than 3 (for high susceptibility classes) or lower than 0.2 (for low susceptibility classes), and the ratio of "very effective" susceptibility classes should be higher than 6 or lower than 0.1.…”

Section: Landslide Susceptibility Modelsmentioning

confidence: 99%

“…In the present work, we decided to use different values to define the threshold of the "effective" and "very effective" classes, taking into account the type of terrain unit used in this study. Thus, it was decided to reduce by 50% the features proposed by Chung and Fabbri (2003). Therefore, a susceptibility class is considered "effective" ("very effective") when the ratio is higher than 1.5 (3) for the high and very high susceptibility classes, and lower than 0.4 (0.2) for the low susceptibility class.…”

Section: Landslide Susceptibility Modelsmentioning

confidence: 99%

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Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Epifânio

Zêzere

Neves

2014

Journal of Sea Research

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The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach-cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region. In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method -the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve. Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.

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Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

et al. 2015

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract Predicting shallow landslide size and location across landscapes is important for understanding landscape form and evolution and for hazard identification. We test a recently developed model that couples a search algorithm with 3-D slope stability analysis that predicts these two key attributes in an intensively studied landscape with a 10 year landslide inventory. We use process-based submodels to estimate soil depth, root strength, and pore pressure for a sequence of landslide-triggering rainstorms. We parameterize submodels with field measurements independently of the slope stability model, without calibrating predictions to observations. The model generally reproduces observed landslide size and location distributions, overlaps 65% of observed landslides, and of these predicts size to within factors of 2 and 1.5 in 55% and 28% of cases, respectively. Five percent of the landscape is predicted unstable, compared to 2% recorded landslide area. Missed landslides are not due to the search algorithm but to the formulation and parameterization of the slope stability model and inaccuracy of observed landslide maps. Our model does not improve location prediction relative to infinite-slope methods but predicts landslide size, improves process representation, and reduces reliance on effective parameters. Increasing rainfall intensity or root cohesion generally increases landslide size and shifts locations down hollow axes, while increasing cohesion restricts unstable locations to areas with deepest soils. Our findings suggest that shallow landslide abundance, location, and size are ultimately controlled by covarying topographic, material, and hydrologic properties. Estimating the spatiotemporal patterns of root strength, pore pressure, and soil depth across a landscape may be the greatest remaining challenge.

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Validation of Spatial Prediction Models for Landslide Hazard Mapping

Cited by 1,061 publications

References 5 publications

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

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