Geomorphological and historical data in assessing landslide hazard

Carrara, A.; Crosta, Giovanni B.; Frattini, Paolo

doi:10.1002/esp.545

Cited by 174 publications

(105 citation statements)

References 21 publications

(36 reference statements)

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“…Predictions based solely on the geographical location of future landslides (susceptibility) are very common due to the fact that they are relatively easy to carry out. A variety of statistical techniques are available for such analysis, including those based on logistic regression analysis (e.g., Atkinson and Massari, 1998;Ohlmacher and Davis, 2003;Suzen and Doyuran, 2004;Nefeslioglu et al, 2008), discriminant analysis (e.g., Baeza and Corominas, 2001;Carrara et al, 2003;Guzzetti et al, 2005), conditional analysis (e.g., Clerici et al, 2002), and weight of evidence (e.g., van Westen et al, 2003;Neuhaeuser and Terhorst, 2007). The obtained susceptibility maps generally do not provide information on the extent of landslides (accumulation area), the number of landslides, the size of landslides (small or a very large landslide), or the temporal frequency of slope failures.…”

Section: Susceptibility and Hazard Analysismentioning

confidence: 99%

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

Jaiswal

Westen

Jetten

2011

Nat. Hazards Earth Syst. Sci.

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Abstract.A quantitative procedure for estimating landslide risk to life and property is presented and applied in a mountainous area in the Nilgiri hills of southern India. Risk is estimated for elements at risk located in both initiation zones and run-out paths of potential landslides. Loss of life is expressed as individual risk and as societal risk using F-N curves, whereas the direct loss of properties is expressed in monetary terms.An inventory of 1084 landslides was prepared from historical records available for the period between 1987 and 2009. A substantially complete inventory was obtained for landslides on cut slopes (1042 landslides), while for natural slopes information on only 42 landslides was available. Most landslides were shallow translational debris slides and debris flowslides triggered by rainfall. On natural slopes most landslides occurred as first-time failures.For landslide hazard assessment the following information was derived: (1) landslides on natural slopes grouped into three landslide magnitude classes, based on landslide volumes, (2) the number of future landslides on natural slopes, obtained by establishing a relationship between the number of landslides on natural slopes and cut slopes for different return periods using a Gumbel distribution model, (3) landslide susceptible zones, obtained using a logistic regression model, and (4) distribution of landslides in the susceptible zones, obtained from the model fitting performance (success rate curve). The run-out distance of landslides was assessed empirically using landslide volumes, and the vulnerability of elements at risk was subjectively assessed based on limited historic incidents.Direct specific risk was estimated individually for tea/coffee and horticulture plantations, transport infrastructures, buildings, and people both in initiation and run-out Correspondence to: P. Jaiswal (jaiswal@itc.nl) areas. Risks were calculated by considering the minimum, average, and maximum landslide volumes in each magnitude class and the corresponding minimum, average, and maximum run-out distances and vulnerability values, thus obtaining a range of risk values per return period. The results indicate that the total annual minimum, average, and maximum losses are about US$ 44 000, US$ 136 000 and US$ 268 000, respectively. The maximum risk to population varies from 2.1 × 10 −1 for one or more lives lost to 6.0 × 10 −2 yr −1 for 100 or more lives lost. The obtained results will provide a basis for planning risk reduction strategies in the Nilgiri area.

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Section: Susceptibility and Hazard Analysismentioning

confidence: 99%

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

Jaiswal

Westen

Jetten

2011

Nat. Hazards Earth Syst. Sci.

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“…However, no general agreement has yet been reached about the best method for producing landslide hazard assessment maps (Guzzetti et al, 2000). Although all known methods have their advantages and disadvantages, utilization of quantitative methods has become preferred and more commonly used in recent years (Binaghi et al, 1998;Aleotti and Chowdhury, 1999;Guzzetti et al, 2000;Carrara et al, 2003;Ercanoglu, 2003;. In addition, utility of GIS (Geographical Information Systems) has been emphasized in nearly every landslide study published in recent years.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks

Ercanoğlu

2005

Nat. Hazards Earth Syst. Sci.

122

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Abstract. Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN) method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index) were used. To obtain maps of these parameters, Digital Elevation Model (DEM) and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi). Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (r ij ) value. Much of the areal extents of the landslides (87.2%) were classified as susceptible to landsliding, and r ij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.Correspondence to: M. Ercanoglu

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“…The discriminant function model is used to determine the relationship between the outcome or the dependent variable (e.g. slope failure) and the independent or predictor variables (causal factors of slope failure) (Nagarajan et al 2000;Carrara et al 2003;Ghosh et al 2012;Jagielko et al 2012). This model is required when there are multiple (i.e.…”

Section: Model Selectionmentioning

confidence: 99%

Deterministic slope failure hazard assessment in a model catchment and its replication in neighbourhood terrain

Acharya

Yatabe

Bhandary

et al. 2014

Geomatics, Natural Hazards and Risk

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In this work, we prepare and replicate a deterministic slope failure hazard model in small-scale catchments of tertiary sedimentary terrain of Niihama city in western Japan. It is generally difficult to replicate a deterministic model from one catchment to another due to lack of exactly similar geo-mechanical and hydrological parameters. To overcome this problem, discriminant function modelling was done with the deterministic slope failure hazard model and the DEM-based causal factors of slope failure, which yielded an empirical parametric relationship or a discriminant function equation. This parametric relationship was used to predict the slope failure hazard index in a total of 40 target catchments in the study area. From ROC plots, the prediction rate between 0.719-0.814 and 0.704-0.805 was obtained with inventories of September and October slope failures, respectively. This means September slope failures were better predicted than October slope failures by approximately 1%. The results show that the prediction of the slope failure hazard index is possible, even in a small catchment scale, in similar geophysical settings. Moreover, the replication of the deterministic model through discriminant function modelling was found to be successful in predicting typhoon rainfall-induced slope failures with moderate to good accuracy without any use of geo-mechanical and hydrological parameters.

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Geomorphological and historical data in assessing landslide hazard

Cited by 174 publications

References 21 publications

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks

Deterministic slope failure hazard assessment in a model catchment and its replication in neighbourhood terrain

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