A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Nandi, Arpita; Shakoor, Abdul

doi:10.1016/j.enggeo.2009.10.001

Cited by 386 publications

(209 citation statements)

References 29 publications

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“…The method is often cited in comparative studies as one of the most efficient data-driven techniques for deriving susceptibility maps (Süzen and Doyuran, 2004;Brenning, 2005;Rossi et al, 2010;Nandi and Shakoor, 2010;Oh et al, 2010;Pradhan and Lee, 2010); moreover, this technique has given good results in similar hilly environments (van den Eeckhaut et al, 2006bEeckhaut et al, , 2009Eeckhaut et al, , 2010.…”

Section: Modelling Methodsmentioning

confidence: 99%

“…Süzen and Doyuran, 2004;Brenning, 2005;van den Eeckhaut et al, 2009;Rossi et al, 2010;Nandi and Shakoor, 2010;Oh et al, 2010;Pradhan and Lee, 2010) and has already given good results in similar hilly environments (van den Eeckhaut et al, 2009;van den Eeckhaut et al, 2010). The method can be directly implemented into GIS software (Kemp et al, 2001;Sawatzky et al, 2009a, b).…”

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

confidence: 99%

“…Süzen and Doyuran, 2004;Brenning, 2005;van den Eeckhaut et al, 2009;Rossi et al, 2010;Nandi and Shakoor, 2010;Oh et al, 2010;Pradhan and Lee, 2010).…”

Section: The Modelling Approach: Limitations and Perspectivesmentioning

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: Modelling Methodsmentioning

confidence: 99%

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

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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“…A ROC curve plots true positive rate TP (sensitivity) against false positive rate FP (1−specificity), for all possible cutoff values; sensitivity is computed as the fraction of unstable cells that were correctly classified as susceptible, while specificity is derived from the fraction of stable cells that were correctly classified as nonsusceptible. The closer the ROC curve to the upper left corner (AUC=1), the higher the predictive performance of the model; a perfect discrimination between positive and negative cases produces an AUC value equal to 1, while a value close to 0.5 indicates inaccuracy in the model (Akgün and Türk 2011;Fawcett 2006;Nandi and Shakoor 2009;Reineking and Schröder 2006). In relation to the computed AUC value, Hosmer and Lemeshow (2000) classify a predictive performance as acceptable (AUC>0.7), excellent (AUC>0.8), or outstanding (AUC>0.9).…”

Section: Validationmentioning

confidence: 99%

“…) Binary logistic regression (BLR) Atkinson and Massari (1998), Ayalew and Yamagishi (2005), Bai et al (2010), Can et al (2005), Carrara et al (2008), Chauan et al (2010), Conforti et al (2012), Dai and Lee (2002), Davis and Ohlmacher (2002), Erener and Düzgün (2010), Mathew et al (2009), Nandi and Shakoor (2009), Nefeslioglu et al (2008, Ohlmacher and Davis (2003), Van den Eckhaut et al (2006 Classification and regression trees (CART) Felicísimo et al (2012), Vorpahl et al (2012) Artificial neuronal networks (ANN) Aleotti and Chowdhury (1999), Ermini et al (2005), Lee et al (2004), Pradhan and Lee (2010) Original Paper exploited to compare the fitting of the model having only the constant term (all the β p are set to 0) with the fitting of the model that includes all the considered predictors with their estimated non-null coefficients so as to verify if the increase in likelihood is significant; in this case, at least one of the p coefficients is to be expected as different from zero (Hosmer and Lemeshow 2000). By exponentiating the β's, odds ratios (OR) for the independent variables are derived: these are measures of association between the independent variables and the outcome of the dependent, and directly express how much more likely (or unlikely) it is for the outcome to be positive (unstable cell) for unit changing of the considered independent variable.…”

Section: Statistical Techniquementioning

confidence: 99%

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

et al. 2013

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Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarge the area included in training the model and to verify the sensitivity of the regressed models with respect to the locations of the selected stable cells. A suite of 16 models was prepared by randomly extracting different unoverlapping stable cell subsets that have been appended to the unstable ones. Models were finally submitted to forward logistic regression and validated. The results showed satisfying and stable error rates (0.236 on average, with a standard deviation of 0.007) and areas under the receiver operating characteristic (ROC) curve (AUCs) (0.839 for training and 0.817 for test datasets) as well as factor selections (ranks and coefficients). As regards the predictors, steepness and large-profile and local-plan topographic curvatures were systematically selected. Clayey outcropping lithology, midslope drainage, local and midslope ridges, and canyon landforms were also very frequently (from eight to 15 times) included in the models by the forward selection procedures. The model-building strategy allowed us to produce a performing earth-flow susceptibility model, whose model fitting, prediction skill, and robustness were estimated on the basis of validation procedures, demonstrating the independence of the regressed model on the specific selection of the stable cells.

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An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions

2015

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Extreme rainfall events are the major driver of shallow landslide occurrences in mountainous and steep terrain regions around the world. Subsurface hydrology has a dominant role on the initiation of rainfall-induced shallow landslides, since changes in the soil water content affect significantly the soil shear strength. Rainfall infiltration produces an increase of soil water potential, which is followed by a rapid drop in apparent cohesion. Especially on steep slopes of shallow soils, this loss of shear strength can lead to failure even in unsaturated conditions before positive water pressures are developed. We present HYDROlisthisis, a process-based model, fully distributed in space with fine time resolution, in order to investigate the interactions between surface and subsurface hydrology and shallow landslides initiation. Fundamental elements of the approach are the dependence of shear strength on the three-dimensional (3-D) field of soil water potential, as well as the temporal evolution of soil water potential during the wetting and drying phases. Specifically, 3-D variably saturated flow conditions, including soil hydraulic hysteresis and preferential flow phenomena, are simulated for the subsurface flow, coupled with a surface runoff routine based on the kinematic wave approximation. The geotechnical component of the model is based on a multidimensional limit equilibrium analysis, which takes into account the basic principles of unsaturated soil mechanics. A series of numerical simulations were carried out with various boundary conditions and using different hydrological and geotechnical components. Boundary conditions in terms of distributed soil depth were generated using both empirical and process-based models. The effect of including preferential flow and soil hydraulic hysteresis was tested together with the replacement of the infinite slope assumption with the multidimensional limit equilibrium analysis. The results show that boundary conditions play a crucial role in the model performance and that the introduced hydrological (preferential flow and soil hydraulic hysteresis) and geotechnical components (multidimensional limit equilibrium analysis) significantly improve predictive capabilities in the presented case study.

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A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Cited by 386 publications

References 29 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)

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions

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