Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya

Banerjee, Polash; Ghose, M. K.; Pradhan, Ratika

doi:10.1007/s12517-018-3488-4

Cited by 60 publications

(21 citation statements)

References 30 publications

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“…The present study analyzed 10 conditioning factors: slope angles, altitude, population density, land use, lithology, soil texture, possession rate of communication tools (mobile phone, radio, or television), rainfall, NDVI, and distance to roads. The choice of factors was based on literature review where several studies (Kannan et al ; Orencio and Fujii ; Liu et al ; Ravera et al ; Banerjee et al ) have applied the same factors to estimate landslides vulnerability. The national disaster risk management plan and policy and the contingency plan for flood and landslides in Rwanda (MIDIMAR ) was considered expert knowledge.…”

Section: Methodsmentioning

confidence: 99%

“…Landslides are described as a wide range of processes responsible for the downward and outward movement of slope‐forming material composed of rock, soil, artificial fills, or a combination of all down a slope (Gravina et al ). Globally, more than 80% of landslides occurrence, losses, and damages result from intense and prolonged precipitation, construction involving undercutting of slopes, mining and quarrying, and earthquakes (Banerjee et al ; Huong et al ; Szewrański et al ). Disaster vulnerability is defined as the social, economic, physical, and environmental factors that increase or decrease an individual, community, or society's susceptibility to the impacts of hazard (Orencio and Fujii ; Liu et al ).…”

Section: Introductionmentioning

confidence: 99%

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Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Nahayo

Ndayisaba

Karamage

et al. 2019

Integr Envir Assess & Manag

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Landslides are among hazards that undermine the social, economic, and environmental well‐being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors—altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools—were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R‐index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000–000. © 2019 SETAC

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Nahayo

Ndayisaba

Karamage

et al. 2019

Integr Envir Assess & Manag

View full text Add to dashboard Cite

show abstract

“…This is followed by the ranking of the model criteria and alternatives based on a certain scale. Based on the ranking the importance or weights of the model criteria and alternatives are estimated and then used in the GIS-aided prediction mapping (Banerjee, Ghose, and Pradhan 2018). However, expert opinion-based prediction mapping may suffer subjective bias.…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

Banerjee¹

2020

Preprint

Self Cite

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The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, population density and proximity to roads are the major determinants of the forest fire. This indicates the role of human activities on the incidences of a forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.77, κ = 0.77). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

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“…This is followed by the ranking of the model criteria and alternatives based on a certain scale. Based on the ranking the importance or weights of the model criteria and alternatives are estimated and then used in the GIS-aided prediction mapping (Banerjee et al, 2018). However, expert opinion-based prediction mapping may suffer subjective bias.…”

Section: Introductionmentioning

confidence: 99%

Drivers and distribution of forest fires in Sikkim Himalaya: A maximum entropy-based approach to spatial modelling.

Banerjee¹

2020

Preprint

Self Cite

View full text Add to dashboard Cite

The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of the forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.78, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

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Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya

Cited by 60 publications

References 30 publications

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

Drivers and distribution of forest fires in Sikkim Himalaya: A maximum entropy-based approach to spatial modelling.

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