A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Özdemir, Adnan; Altural, Tolga

doi:10.1016/j.jseaes.2012.12.014

Cited by 399 publications

(179 citation statements)

References 54 publications

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“…The other lithological units show different sensitivities to landslides (Table 2). Our results are in agreement with those of Gokceoglu et al (2005), Lee and Pradhan (2007), and Ozdemir and Altural (2013), which have found that variations in landslide distribution are highly dependent on lithology type. For distance to streams, the frequency values show that when the distance increases, the probability of landslides decreases.…”

Section: Application Of Frequency Ratio Modelsupporting

confidence: 94%

“…For distance to faults, the landslide occurrence probability is high in areas where the distance is 100-200 and 200-300 m, whereas the probability decreases at a distance of less than 100 m or more than 300 m from a fault line. A similar trend was also reported by Ozdemir and Altural (2013). On the other hand, a different result was reported by Khanh (2009), who found that most landslides occur within a distance of 250-1,000 m. We attribute this difference to the local conditions, such as fault density, fault orientation, and rock structure.…”

Section: Application Of Frequency Ratio Modelsupporting

confidence: 86%

“…Landslide inventory mapping is the systematic mapping of existing landslides in the study area using different techniques, such as field survey, aerial photograph, and satellite image interpretation, and literature search for historical landslide records (Ozdemir and Altural 2013). The landslide inventory map for our study area was compiled through aerial photographs interpretation and field-based inspection.…”

Section: Landslide Inventory Mapmentioning

confidence: 99%

“…The frequency ratio model can be simply implemented within a GIS environment, and the results are easy to understand (Yalcin et al 2011;Mohammady et al 2012;Ozdemir and Altural 2013). Another model used for LSM in this study is index of entropy model.…”

Section: Landslide Susceptibility Mapping (Lsm)mentioning

confidence: 99%

See 3 more Smart Citations

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.

328

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 Modelsupporting

confidence: 94%

Section: Application Of Frequency Ratio Modelsupporting

confidence: 86%

Section: Landslide Inventory Mapmentioning

confidence: 99%

Section: Landslide Susceptibility Mapping (Lsm)mentioning

confidence: 99%

See 2 more Smart Citations

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.

328

137

View full text Add to dashboard Cite

show abstract

“…Quantitative methods mainly refer to various statistical analyses (Carrara et al 1991;Tang et al 2011a;Yoshimatsu and Abe 2006;Kamp et al 2008), such as artificial neural network (ANN) (Lee and Evangelista 2006;Yilmaz 2010), support vector machine (SVM) (Yao et al 2008;Yilmaz 2010;Xu et al 2012), Bayesian Network (Song et al 2012) and Logistic Regression (Dong et al 2011;Nefeslioglu et al 2006;Xu et al 2013a, b). These methods have different characteristics and adaptability for various environmental conditions of different regions (Tangestani 2009;Mohammady et al 2012;Ozdemir and Altural 2013). At the same time, GIS and RS technologies have comprehensively been used in the landslide susceptibility study Lan et al 2002Lan et al , 2004Kouli et al 2010;Niu et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent postearthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50°and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of 123Nat Hazards (2015) 75:2467-2487 DOI 10.1007/s11069-014-1441 landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

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Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Zhang

Liu

et al. 2023

Geological Journal

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Landslide susceptibility analysis can provide theoretical support for landslide risk management. However, some susceptibility analyses are not sufficiently interpretable. Moreover, the accuracy of many research methods needs to be improved. Therefore, this study can supplement these deficiencies. This study aims to research the evaluation effects of random forest (RF) and extreme gradient boosting (XGBoost) classifier models on landslide susceptibility, and to compare their applicability in Fengjie County, Chongqing, a typical landslide‐prone area in southwest of China. Firstly, 1624 landslides information from 1980 to 2020 were obtained through field investigation, and a geospatial database of 16 conditional factors had been constructed. Secondly, non‐landslide points were selected to form a complete data set and RF and XGBoost models were established. Finally, the area under the ROC curve (AUC) value, accuracy, and F‐score were used to compare the two models. The results show that even though both classifiers have a highly accurate evaluation of landslide susceptibility, the RF model performs better. In comparison, the RF model has a higher AUC value of 0.866, and its accuracy and F‐score are approximately 2% higher than XGBoost. The land use, elevation, and lithology of Fengjie County contribute to the occurrence of landslides. This is due to human engineering activities (such as land reclamation, and housing construction) resulting in low slope stability and landslides in widely distributed sandstone, siltstone, and mudstone layers owing to their low permeability and planes of weakness.

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A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Cited by 399 publications

References 54 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

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

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