Probabilistic Techniques, GIS and Remote Sensing in Landslide Hazard Mitigation: A Case Study from Sikkim Himalayas, India

Ramakrishnan, D.; Ghose, M. K.; Chandran, Rahul; Jeyaram, A.

doi:10.1080/10106040508542364

Cited by 22 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the main advantages of the IoE method is that this method can determine the importance of effective factors and analyze the spatial relationship between effective factors and gullies occurring in the study area [52]. The IoE model demonstrated how the most important factor could be estimated from the factors affecting the phenomenon (gully erosion) occurrence [83]. In other words, it can identify the variables that have the highest impact on the event of a phenomenon, which is important because, depending on the physiographic conditions of the area, there are usually several factors affecting gully erosion.…”

Section: Discussionmentioning

confidence: 99%

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

et al. 2019

View full text Add to dashboard Cite

Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province.

show abstract

Section: Discussionmentioning

confidence: 99%

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

et al. 2019

View full text Add to dashboard Cite

show abstract

“…These statistical models were developed using the relationship between landslide events of the past and the prevailing geo-technical parameters around the landslide locations. Logistic Regression Model was used for landslide mapping by Atkinson and Massari (1998), Lee (2005) and Ramakrishna et al (2005) and Information Value Method by Ramakrishna et al (2005). Fuzzy Algebraic Function was used for landslide susceptibility modelling by Pistocchi et al (2002), Ercanoglu and Gokceoglu (2004) and Lee (2007).…”

Section: Introductionmentioning

confidence: 99%

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

Patel

Ghose

et al. 2013

Geocarto International

View full text Add to dashboard Cite

The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%).

show abstract

“…The occurrence of varying composite index values within the study area will enable the analyst to categories the polygon elements on the basis of them. To implement this idea, information value theory, a widely used statistical model is applied by many researchers in landslide susceptibility study (Yin and Yan 1988;Ramakrishna et al 2005). The present investigation was carried out in the Rumtek Samdung area of Sikkim, India involving fourteen causative parameters identified in the area that were subcategorized into forty-eight subclasses.…”

Section: Introductionmentioning

confidence: 99%

“…Several statistical models have also been developed and employed for performing such analysis. Logistic regression model was used for landslide mapping by Atkinson and Massari (1998), Lee (2004Lee ( , 2005 and Ramakrishna et al (2005) along with Frequency Ratio method (Lee and Sambath 2006;Lee and Pradhan 2006;Yilmaz 2009a;Avinash and Ashamanjari 2010) and information value method (Ramakrishna et al 2005). Fuzzy Algebraic Function was used for landslide susceptibility modeling by Pistocchi et al (2002), Ercanoglu and Gokceoglu (2004) and Lee (2007).…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Probabilistic Techniques, GIS and Remote Sensing in Landslide Hazard Mitigation: A Case Study from Sikkim Himalayas, India

Cited by 22 publications

References 8 publications

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

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

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

Contact Info

Product

Resources

About