GIS Application in Landslide Susceptibility Mapping of Indian Himalayas

Sarkar, Sutanu; Kanungo, Debi Prasanna

doi:10.1007/978-4-431-54391-6_12

Cited by 19 publications

(14 citation statements)

References 10 publications

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“…In this study, gradient from 0 o to 30 o have found to have positive weight values (Table 1). Results of this study contrasts with that of Kayastha et al (2012Kayastha et al ( , 2013 and Sarkar et al (2006) ) as their research sites had positive weight values for slopes greater than 25 o . These slopes are major cultivated lands and grasslands which are devoid of trees.…”

Section: Factor Classes With Positive Weighted Valuescontrasting

confidence: 90%

“…Bedding structure, prominent joints, differential rates of weathering and erosion of the sedimentary rocks of the Siwaliks and meta-sedimentary and metamorphic rocks of the Lesser Himalaya (northern part of the Banganga River Basin) were geological characteristics controlling the landslide types and processes (Ghimire 2010). Banded gneiss in Kankai watershed (Kayastha et al 2012) and phyllite and gneiss of Sikkim (Sarkar et al 2006) were contributing to landslids and hence produced positive weight values. Areas with lithology of phyllite, schist and gneiss contribute to landslides where high degree of foliation and weathering play crucial role.…”

Section: Fig 5: Landslide Inventory Of Research Sitementioning

confidence: 99%

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Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

Budha

Paudyal

Ghimire

2016

Journal of Nepal Geological Society

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The tendency of occurrences of slope failures in future in an area is landslides susceptibility. This tendency in eastern hills of Rara Lake was analyzed through mapping process. Bivariate statistical index method was used to produce the susceptibility of landslides. Ninety six slope failures were delineated from Google Earth imagery. The ratio of landslide densities of each individual factor classes to that of whole area gave weight values necessary to produce landslide susceptibility index (LSI). East and South facing slopes, gradients of <30o, elevation of 2000-2800 m, buffers closer to road and streams, grassland and cultivation lands, and lithology of pelitic metamorphic rocks were factor classes with positive weight values. The LSI values ranging from -7.12 to 5.45 were reclassified into five susceptibility classes. Landslide densities of 8.12 and 4.76 per km2 were observed for very high and high susceptible zones. Success rate curve, made from 35 landslides located in the field survey, showed 0.76 portion area under the curve. This gives 76% overall success rate. Higher susceptible areas are cultivated areas and grasslands, where most houses were located. Thus, knowing the landslide susceptibility of areas, necessary preparedness can be done to reduce the impacts.

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Section: Factor Classes With Positive Weighted Valuescontrasting

confidence: 90%

Section: Fig 5: Landslide Inventory Of Research Sitementioning

confidence: 99%

Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

Budha

Paudyal

Ghimire

2016

Journal of Nepal Geological Society

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“…Various models and methodologies have been utilized to decide the impact of causal factors on landslide occurrence, of which the multi-criteria decision analysis (MCDA) method based on fuzzy logic [23], analytical hierarchy process (AHP) [24][25][26], and weighted linear combination [27] are most commonly used. In terms of statistical methods, the frequency ratio method is the simplest and easiest to perform, while the information value (IV) [28,29] and weight of evidence (WOE) [30,31] methods are useful in determining the impact of causal factor class on landslide occurrence. Logistic regression (LR) is also used by many researchers for determining the weight of the causal factors [17,26,32,33].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Pasang

Kubíček

2020

Geosciences

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In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.

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“…The information value method (IVM) is a datadriven technique that can evaluate the objective assessment for landslide susceptibility. This method calculates the relationship between the dependent variable (landslides) and the independent variable (causative factor) based on the weightage of the influence (Yin and Yan, 1988;Sarkar et al, 2012). The calculation of IVM weight is based on the rationing of each factor class landslide density to the total landslide density in the respective causative factor (Pradhan et al, 2012;Sarkar et al, 2013;Wang et al, 2014).…”

Section: Information Value Methodsmentioning

confidence: 99%

Comparison of data-driven landslide susceptibility assessment using weight of evidence, information value, frequency ratio and certainly factor methods

Farooq¹

2021

Acta Geodynamica Et Geomaterialia

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Landslide susceptibility assessment is essential for development activities and disaster management in the mountainous regions to identify the landslide-prone areas. The present study aimed to evaluate and compare the efficacy of data driven quantitative models of landslide susceptibility assessment using geospatial tools in Jhelum valley of the Himalayas. This area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. Four probabilistic data-driven models are employed for this purpose, which includes the weight of evidence (WOE), information value method (IVM), frequency ratio (FR), and certainty factor (CF). These assessed models are based on integrating landslide contributing factors and a ground truthing-based landslide inventory of 437 landslides. The landslide susceptibility maps were presented by categorizing the study area into very low to very high susceptibility zone by Jenks natural breaks method. The performance of models was evaluated by a sensitivity analysis using Receiver Operator Curve (ROC) method. The ROCvalidated results of success rate curves for WOE, IVM, FR and CF were 80 %, 78 %, 77 %, and 76 % respectively. The prediction rate curve of WOE, IVM, FR, and CF was 78 %, 77 %, 75 %, and 78 % respectively. The results showed the reasonable efficiency of applied models for landslide susceptibility assessment in the study area and applicable to regions with similar geomorphological conditions. Conclusively, the comparison of applied models revealed the promising results of used approaches.

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GIS Application in Landslide Susceptibility Mapping of Indian Himalayas

Cited by 19 publications

References 10 publications

Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Comparison of data-driven landslide susceptibility assessment using weight of evidence, information value, frequency ratio and certainly factor methods

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