How can statistical models help to determine driving factors of landslides?

Vorpahl, Peter; Elsenbeer, Helmut; Maerker, Michael; Schröder, Boris

doi:10.1016/j.ecolmodel.2011.12.007

Cited by 284 publications

(212 citation statements)

References 48 publications

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“…On the one hand, SPI is an indicator for erosive processes, as are caused by surface runoff. On the other hand, it is an indicator for the intermediate scale topographic position (ridge, slope, or valley bottom) (Vorpahl et al 2012). The SPI is calculated from the following formula:…”

Section: Landslide-conditioning Factorsmentioning

confidence: 99%

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

Jaafari

Najafi

Pourghasemi

et al. 2014

Int. J. Environ. Sci. Technol.

339

137

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This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70 %) out of 103 detected landslides were randomly selected for modeling, and the remaining 31 (30 %) cases were used for the model validation. The landslide-conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index, stream power index, sediment transport index, normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume, were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50 % of the study area. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC = 75.59 %) is slightly better in prediction than frequency ratio model (AUC = 72.68 %). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations.

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Section: Landslide-conditioning Factorsmentioning

confidence: 99%

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

Jaafari

Najafi

Pourghasemi

et al. 2014

Int. J. Environ. Sci. Technol.

339

137

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“…At the same time, some other papers in literature deal with the estimation of robustness in terms of stability of the statistical procedure, disregarding the problem of the geologic representativeness of the subset on which regression is applied (e.g., Carrara et al 2008;Vorpahl et al 2012), using totally boot strapping-based procedures. The strategy here adopted seems to be adequate enough to apply logistic regression, which requires a balanced sizing of the worked dataset, without losing the connection between reliability and accuracy of the susceptibility model and its real spatial representativeness.…”

Section: Discussion and Concluding Remarksmentioning

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%

“…Among the statistical techniques and also in comparative studies (Akgün 2012;Felicísimo et al 2012;Guzzetti et al 2005;Mathew et al 2009;Rossi et al 2010;Vorpahl et al 2012), logistic regression (Hosmer and Lemeshow 2000) has proved to be one of the most suitable and performing methods for assessment of landslide susceptibility on basin scale. The wide use of this multivariate technique for landslide susceptibility modeling is mainly due to its capacity to work on any type of independent variable (either ratio, interval, and ordinal or nominal scale), regardless of the deviance of the considered predictors and residuals from a normal distribution.…”

Section: Introductionmentioning

confidence: 99%

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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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“…, and landslide assessment (VORPAHL et al 2012). Especially for determining the assessment of slope stability or landslide susceptibility, different approaches can be applied.…”

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confidence: 99%

Assessment of Shallow Landslide Initiation Areas Using stochastic Modelling: The Vernazza Torrent Case Study, Liguria, Italy

Schmaltz¹,

Rosner²,

Maerker³

2015

giforum

Self Cite

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The objective of this study is the assessment of potential failure zones of landslides in unstable areas. For this purpose, two different stochastic classification models were used: A boosted decision tree approach with TreeNet (TN), and a bagging decision tree approach with Random Forests (RF). Both topographic and soil parameters were considered as predictor variables for training and testing the models. We assume that several predictor variables will lead to misclassification and incorrectness, especially soil parameters. Hence, the misclassification of these particular predictors should be avoided, using the strategy of tree boosting. The investigated area is the hydrological basin of Vernazza in Cinque Terre, Northwest Italy. A disastrous flash flood on the 25 th of October 2011 with numerous landslides caused fatalities and economic losses amounting to millions of Euros. We mapped landslide areas in the field and checked the resulting maps with high resolution remote sensing images. Furthermore, the relevant soil parameters were collected based on a geostatistical approach. We measured topographic parameters, and physical and hydrological soil characteristics such as maximum shear strength under saturated and unsaturated conditions, and hydraulic conductivity (Ksat), and attributed random points in three distinguished classes: i) initiation areas, representing the most likely failure areas for possible landslides, ii) transport areas which were considered as a mix of classes 1 and 3, and iii) stable areas, such as valley bottom, ridges, and unconditionally stable areas. We ran both models with a training dataset (0.8 of the total points Ntot) and a test dataset (0.2 of Ntot) and each with 2000 grown decision trees. We validated the models with a Receiver Operating Characteristic (ROC) curve integral. The regionalized results of the TreeNet dataset yielded potential susceptible landslide areas of a total area of 1.74 km², which is 29.74% of the total area. In contrast, the Random Forests model classified a much greater susceptible area (84.27% of the total area). The results show that Treenet is outperforming RF. The latter misclassifies especially the soil related variables, whereas TreeNet yields robust model results.

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How can statistical models help to determine driving factors of landslides?

Cited by 284 publications

References 48 publications

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

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

Assessment of Shallow Landslide Initiation Areas Using stochastic Modelling: The Vernazza Torrent Case Study, Liguria, Italy

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