Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India

Sharma, L. P.; Patel, Nilanchal; Ghose, M. K.; Debnath, Pradip

doi:10.1080/10106049.2012.748830

Cited by 30 publications

(9 citation statements)

References 24 publications

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“…These rocks are highly vulnerable to landslide as the presence of well-developed foliation plains and joints. Our results are in agreement with Sharma et al (2014); Chawla et al (2018) which have found that landslide distribution are high on phyllite, lingtse granite gneiss and Darjeeling gneiss. Within lineament classes, high PLR is observed in high lineament subclass (Table 5).…”

Section: Rf-plr Modelsupporting

confidence: 93%

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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Section: Rf-plr Modelsupporting

confidence: 93%

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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“…The analysis shows that most studies have used quantitative techniques, of which logistic regression has been most used [81][82][83]. Thereafter, frequency ratio and information value were used 16% and 9% of the studies, respectively [74,[84][85][86][87][88]. In terms of semi-quantitative models, AHP has been the most used method (Figure 8).…”

Section: Landslide Susceptibilitymentioning

confidence: 99%

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

et al. 2020

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Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area.

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“…In the same study area, the landslide vulnerability assessment and zonation was performed earlier using the same causative parameters and applying several methods. These include tri-variate approach (Sharma et al 2009a), landslide susceptibility assessment based on soil characteristics (Sharma et al 2009b(Sharma et al , 2012b, ranking of causative parameters (Sharma et al 2011), experts' weight and Shannon's entropy (Sharma et al 2012a), landslide density of the causative parameters (Sharma et al 2012c) and likelihood ratio approaches (Sharma et al (2012d), Fuzzy Logic (Sharma et al 2013).The present method of study introduces a new approach to perform the task of landslide susceptibility assessment. This approach integrates the concept of Shannon's entropy with the information value theory, to compute the landslide information values of the different polygons.…”

Section: Introductionmentioning

confidence: 96%

Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India

et al. 2014

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The state of Sikkim in India has many steep slopes and has been susceptible to landslides. Since 1968 there have been innumerable losses of lives and properties due to landslides. There is an urgent need for advance assessment of degrees of vulnerability and delineation of the most vulnerable zone for shifting of the population and infrastructure to a safer zone. The identification and formulation of most suitable and acceptable method for such assessment is still nascent and research based. In this study, an attempt has been made to integrate the concept of Shannon's entropy with the information value-based statistical model to evaluate the landslide susceptibility in the study area and assess the improvement made through the integration of Shannon's entropy by comparing the results with the landslide susceptibility determined from the information value-based statistical model alone. Initially, the thematic layers pertaining to all the causative parameters were overlaid with the help of geographical information system that resulted in the formation of 78,256 numbers of polygons for each one of which landslide susceptibility was determined. For each polygon, the total landslide information value (TLIV) was computed as the summation of the landslide information values determined for the individual sub-categories present within the respective polygons. Again for each polygon, the Shannon's entropy value of the individual parameters was multiplied with the summation of the landslide information values of all the sub-categories present within the respective parameters. The product values computed for the different causative parameters were summed up to determine the total landslide information value with entropy (TLIV_e). Finally, the entire study area was categorized into five zones of landslides susceptibility based on the TLIV and TLIV_e, respectively. The prediction accuracy of the landslides determined based on the landslide susceptibility derived from TLIV_e was found to be significantly high (91 %) as compared to that derived from TLIV (85 %) indicating the potential contribution of Shannon's entropy in the improved delineation of the landslide susceptibility zones.

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Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India

Cited by 30 publications

References 24 publications

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

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

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India

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