Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model

2021 ◽  
Author(s):  
Halil Akinci ◽  
Ayse Yavuz Ozalp
Keyword(s):  
Hazard Assessment ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Information Value Model ◽  
Value Model

Landslide Susceptibility Zonation Mapping in and Around Dharamshala, Himachal Pradesh Using Bivariate Statistical Techniques – A Comparative Study

2020 ◽  
Author(s):  
Kumari Sweta ◽  
Ajanta Goswami
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Himachal Pradesh ◽  
Logic Model ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Statistical Techniques

<p><strong>Abstract</strong><strong>:</strong> Landslides are one of the most common and devastating natural hazards worldwide, which cause injuries to life and damage to properties, infrastructures leading to high-cost maintenance. In this study frequency ratio, information value and fuzzy logic models were used for landslide susceptibility mapping of an area of 356km<sup>2</sup> in and around Dharamshala, Himachal Pradesh, using earth observation data. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. The thrust for infrastructure development has led to a need for prior planning to minimize the consequences of landslide hazards. The final produced landslide susceptibility zonation maps with better accuracy could be used for land-use planning to prevent future losses. A landslide inventory for the study area was prepared through visual interpretation of high-resolution satellite imagery and available inventory report. Remote sensing data and other ancillary data like geological data were collected and processed in the GIS environment to generate thematic maps of parameters influencing landslide occurrence. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. Using these parameters and landslide inventory weight and membership value was calculated for the Frequency ratio, information value and Fuzzy logic model, respectively. In the frequency ratio and information value model, all the landslide causative parameters were arithmetically overlaid using calculated weights for landslide susceptibility mapping. In the fuzzy logic model, different fuzzy operators were applied to the calculated fuzzy membership values. Unlike the normalization process for membership calculation present study used the cosine amplitude method, which will give more reliable results. A total of ten landslide susceptibility maps (LSM) were produced using two models, 9 from fuzzy logic and 1 from frequency ratio. All the results were verified spatially and statistically using landslide locations and ROC curves. Further, the performance and significance of different outputs were compared to select the most suitable LSM for the study area. Among all fuzzy operators, “gamma” with λ = 0.9 showed the best accuracy (84.3%) and operator “and” has the worst accuracy (77.6%). But among all 9 output maps of fuzzy logic except the output of gamma (λ = 0.9) gives satisfactory LSM rest all show the unacceptable result as the maximum number of pixels is either in very low or high susceptible zone. The validation and comparison result exhibited that the fuzzy logic (accuracy=84.3%) is better than the information value (83.46) and the frequency ratio method (accuracy=83.43%).</p><p><strong>Keywords</strong>: Bivariate Statistical Techniques, Information Value, Frequency Ratio, Fuzzy Logic, ROC</p>

scholarly journals Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Landslide Hazard ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

AbstractUatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

scholarly journals Landslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia

2020 ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Landslide Hazard ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract Abstract Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes. Keywords: landslide; susceptibility; Geographic Information System (GIS); certainty factor; frequency ratio; information value; Ethiopia.

The Landslide Susceptibility Assessment using Bi-variate Statistical Information Value Model of Chenab River Valley, Jammu and Kashmir (India)

2021 ◽  
Vol 14 (11) ◽  
pp. 44-56
Author(s):  
Abhijit S. Patil ◽  
Bidyut K. Bhadra ◽  
Sachin S. Panhalkar ◽  
Sudhir K. Powar
Keyword(s):  
Landslide Susceptibility ◽  
Statistical Information ◽  
River Valley ◽  
Information Value ◽  
Landslide Inventory ◽  
Susceptibility Map ◽  
Acceptable Result ◽  
Information Value Model ◽  
Value Model ◽  
Chenab River

Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.

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