Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India

Kannan, M.; Saranathan, E.; Anabalagan, R.

doi:10.1007/s12517-012-0587-5

Cited by 77 publications

(30 citation statements)

References 44 publications

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“…In order to identify the closeness of their relationship, a simple statistical technique has been applied to derive it with the frequency ratio approach. Furthermore, FR model became valuable in ranking the preferred causative factors based upon their ability to control a landslide incident (Kannan et al 2013), because FR can describe clearly the difference of each score between landslide causal factors in class and landslide occurrence. Thus, the number of landslide occurrence pixels on the area must be combined between causal factors.…”

Section: Frequency Ratiomentioning

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

176

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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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Section: Frequency Ratiomentioning

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

176

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“…Lineaments play a vital role in slope failure (Ramli et al 2010). Lineaments describe the plane/zone of weakness and most landslides can occur at this weaker zone (Kannan et al 2013;Thapa et al 2017a). A regional lineament map is developed from IRS LISS III digital data (5 m resolution).…”

Section: Lineament Densitymentioning

confidence: 99%

“…Probabilistic likelihood ratio (PLR) PLR is also known as frequency ratio (FR) model where the statistical relationship between factors and landslide events is determined (Kannan, Saranathan, and Anbazhagan 2012). It follows the basic principle of favourability function (Chung and Fabbri 2005).…”

Section: Statistical Modellingmentioning

confidence: 99%

Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques

et al. 2019

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Landslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE.

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“…Further, the area under the curve (AUC) can be used to quantitatively indicate the success rate of the susceptibility prediction and to evaluate the fitting degree of the prediction model and the actual landslides [4]. In addition, this paper introduces the R index (relative landslide density) to validate the landslide susceptibility evaluation results [40,41]. (12) where n i is the number of landslides that occurred at the sensitivity level i, as well as N i is the number of pixels at the same sensitivity level i.…”

Section: Validation Of Landslide Susceptibility Maps Using the Area Umentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Zhao

Yao

Mei

et al. 2017

Entropy

106

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Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen'an County, Shan'xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert's knowledge and the opinions of decision-makers.

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Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India

Cited by 77 publications

References 44 publications

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

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

Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

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