GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey)

Yilmaz, Işık; Keskin, İnan

doi:10.1007/s10064-009-0188-z

Cited by 84 publications

(41 citation statements)

References 26 publications

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“…For NDVI values 0.3-0.5, the landslide frequency ratio was larger than 1, illustrating that relatively high vegetation coverage can easily lead to landslide occurrence. A positive correlation between landslide occurrence and NDVI was also reported by Yilmaz and Keskin (2009). The widely held view is that the vegetation coverage can protect slopes by reducing erosion, strengthening soil, and inhibiting landslides, which increase general slope stability (Bell 1998;Abdi et al 2010).…”

Section: Application Of Frequency Ratio Modelmentioning

confidence: 76%

“…Low values of the NDVI (0.1 and below) indicate barren areas, sand, or snow. Moderate values (0.2-0.3) represent shrub and grasslands, whereas high values (0.6-0.8) correspond to temperate and tropical rainforests (Yilmaz and Keskin 2009). Forest canopy, timber volume, and plant community are also significant environmental parameters considered in this study .…”

Section: Landslide-conditioning Factorsmentioning

confidence: 99%

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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.

337

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: Application Of Frequency Ratio Modelmentioning

confidence: 76%

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.

337

137

View full text Add to dashboard Cite

show abstract

“…FR was combined with analytical heuristic approach (AHP) by Demir et al (2013) and Reis et al (2012), and combination using FR, AHP, LR and artificial neural network (ANN) model was proposed by Park et al (2013). Integrated techniques such as FR, weight of evidence (WoE) and deterministic methods have been applied by Cervi et al (2010) and Yilmaz and Keskin (2009). Association models like WoE, AHP and fuzzy logic to combine multiple factor layers to create landslide susceptibility map was introduced by Suh et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Rasyid

Bhandary

Yatabe

2016

Geoenviron Disasters

175

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The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km 2 . Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

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“…Treating landslide occurrence as a conditional probability problem, supervised learning models have been used to describe the spatial distribution of landslide probability as a function of various subsets of these variables (Korup and Stolle, 2014). Most previous work has focussed on analysis of landslides 10 specific to a single earthquake or region (Yilmaz and Keskin, 2009, Lee et al, 2008, Kamp et al, 2008, Garcia-Rodriguez et al, 2008, Lee and Evangelista, 2006, Ayalew and Yamagishi, 2005, Lin and Tung, 2004. Models based on these studies are necessarily regionally specific, lacking a sufficient spread of conditions to make them transferable between events.…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of earthquake-induced landslide probability

Parker

Rosser

Hales

2017

Preprint

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Abstract.We developed a generalized model to describe and predict the spatial distribution of earthquake-induced landslides, based on a regression analysis of 9 co-seismic landslide inventories from different earthquakes and regions. Our model expresses the absolute spatial probability of landslides as a function of peak ground acceleration and hillslope gradient, based on data from global topographic and seismic ground motion datasets. The output from our model predicts probabilities for landslides triggered in sedimentary, meta-sedimentary, igneous and volcanic lithology, and is applicable to 10 shallow continental earthquakes of moment magnitude range 6.2 to 7.9, and depths between 10 and 21 km. To obtain absolute probability predictions, we use only landslide source areas as input data, and explicitly estimate and correct for known incompleteness in input datasets, through a novel Monte Carlo approach. We estimate the uncertainty of these predictions, through extensive testing of the performance of the model, when making out-of-sample predictions for all 9 earthquakes. Our model is notably simpler than others developed to predict spatial probability of landsliding, as we have 15 only included variables that could be constrained consistently at the global-scale, and eliminated those that did not influence landslide probability in a consistent manner across all earthquakes in our dataset. The model outputs also provide a baseline to further investigate spatial and temporal sources of unexplained variability in co-seismic landslide distributions. Using freely available topographic and ground motion data, we suggest that our model can be applied more widely, to provide landslide predictions for earthquakes with no landslide data. 20

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GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey)

Cited by 84 publications

References 26 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

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Spatial prediction of earthquake-induced landslide probability

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