GIS techniques and statistical models in evaluating landslide hazard

Carrara, A.; Cardinali, Mauro; Detti, R.; Guzzetti, Fausto; Pasqui, Valdo; Reichenbach, Paola

doi:10.1002/esp.3290160505

Cited by 856 publications

(528 citation statements)

References 11 publications

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“…The indirect methods have been applied and improved by scientists (e.g. Carrara, 1991;Guzzetti et al, 1999;van Westen, 2000;van Westen et al, 2003), but few of these were used in official RAMs (Malet and Maquaire, 2008). However, they can assist in choosing the most revealing data sets to take into account and can be a real alternative in susceptibility/hazard zoning (van Westen et al, 2008;Ercanoglu et al, 2008).…”

Section: Fressard Et Al: Which Data For Quantitative Landslide Sumentioning

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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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: Fressard Et Al: Which Data For Quantitative Landslide Sumentioning

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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“…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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“…ables. Haigh et al 1988 andCarrara et al 1991 related the distribution of landslides to land use practices, particularly highway construction and maintenance.…”

Section: Preõious Studiesmentioning

confidence: 99%

The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico

1998

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Landslides are common in steep mountainous areas of Puerto Rico where mean annual rainfall and the frequency of intense storms are high. Each year, landslides cause extensive damage to property and occasionally result in loss of life. Average population density is high, 422 peoplerkm 2 , and is increasing. This increase in population density is accompanied by growing stress on the natural environment and physical infrastructure. As a result, human populations are more vulnerable to landslide hazards. The Blanco, Cibuco, and Coamo study areas range in surface area from 276 to 350 km 2 and represent the climatologic, geographic, and geologic conditions that typify Puerto Rico. Maps of recent landslides developed from 1:20,000-scale aerial photographs, in combination with a computerized geographic information system, were used to evaluate the frequency and distribution of shallow landslides in these areas. Several types of landslides were documentedrainfall-triggered debris flows, shallow soil slips, and slumps were most abundant. Hillslopes in the study area that have been anthropogenically modified, exceed 128 in gradient, are greater than 300 m in elevation, and face the east-northeast, are most prone to landsliding. A set of simplified matrices representing geographic conditions in the three study areas was developed and provides a basis for the estimation of the spatial controls on the frequency of landslides in Puerto Rico. This approach is an example of an analysis of the frequency of landslides that is computationally simple, and therefore, may be easily transferable to other settings. q

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GIS techniques and statistical models in evaluating landslide hazard

Cited by 856 publications

References 11 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)

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

The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico

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