Landslide Susceptibility Mapping and Comparison Using Frequency Ratio and Analytical Hierarchy Process in Part of NH-58, Uttarakhand, India

Veerappan, Ramesh; Negi, Ankur; Anbazhagan, S.

doi:10.1007/978-3-319-53498-5_123

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Conditioning factors used in GIS analyses algorithm was used for grouping slope angles, elevation, relief energy, TWI, SPI, and LS (Małka 2015;Veerappan et al 2017;Arabameri et al 2020). The remaining thematic maps included land use and land cover (Urban Atlas 2012), lithological, and groundwater table maps (Frankowski et al 2007).…”

Section: Figmentioning

confidence: 99%

“…The application of a high number of variables, various methods of presenting landslides, and various statistical methods enabled the selection of the best model and compare the results. All susceptibility models were developed using the classes determined by the Jenks algorithm (Conforti et al 2011;Devkota et al 2013;Youssef et al 2016;Veerappan et al 2017).…”

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

“…Landslide susceptibility refers to the probability of occurrence of mass movement in a given area due to the existing environmental conditions (Varnes 1984). In Poland, urban area susceptibility assessments have only been conducted for the city of Gdańsk (Małka 2015).…”

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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This work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

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Section: Figmentioning

confidence: 99%

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

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Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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“…In the Darjeeling Himalayas, India, FR (Sarkar and Kanungo 2004; Das and Lepcha 2019), WOE (Saha and Saha 2020), AHP (Ghosh et al 2011; Mandal and Mandal 2018; Das et al 2023), and Particle Swarm Optimization-Support Vector Machine (SVM) (Chawla et al 2018; Saha and Saha 2020) models were used for landslide susceptibility zonation. Because of its geological and topographical conditions, and seismic activities, the Indian Himalayas are prone to the occurrence of landslides (Veerappan et al 2017; Meena and Tavakkoli Piralilou 2019). A research study was conducted in a road section of NH in the Indian Himalayas using LR and comparing it with rock mass classification (Das et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Assessment of landslide susceptibility of Pithoragarh, Uttarakhand (India) using logistic regression and multi-criteria decision-based analysis by analytical hierarchy process

Bhardwaj

Singh

2023

Applied Earth Science: Transactions of the Institutions of Mini

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Landslides are the most prevalent natural hazard in hilly regions of India. This study examines the landslide susceptibility of the Pithoragarh, Uttarakhand, India, using multi-criteria decision based analysis by analytical hierarchy process (AHP), and logistic regression (LR) analysis. The LSZ modelling was performed using fourteen landslide causative factors. Based on past landslide data, landslide locations were identified, which were further divided into a 70/30 ratio, with 70 representing training and 30 representing validation. Validation of the findings of the predicted maps of landslide susceptibility using Area under Curve (AUC) indicates that the predicted map using the LR approach has the highest prediction rate compared to other methods used for landslide susceptibility prediction. Also, validation of all the models was done using Landslide Density Index (LDI) which shows the validity of all models. Thus, the results of all models can be used to predict landslide susceptibility in Pithoragarh.

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Landslide Hazard, Risk Assessment and Prediction: Landslide Inventories and Susceptibility, Hazard Mapping Methods, Damage Potential—Part 2

Arbanas

2017

Advancing Culture of Living With Landslides

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Landslide Susceptibility Mapping and Comparison Using Frequency Ratio and Analytical Hierarchy Process in Part of NH-58, Uttarakhand, India

Cited by 8 publications

References 23 publications

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Assessment of landslide susceptibility of Pithoragarh, Uttarakhand (India) using logistic regression and multi-criteria decision-based analysis by analytical hierarchy process

Landslide Hazard, Risk Assessment and Prediction: Landslide Inventories and Susceptibility, Hazard Mapping Methods, Damage Potential—Part 2

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