Comparative analysis in GIS-based landslide hazard zonation—a case study in Bodi-Bodimettu Ghat section, Theni District, Tamil Nadu, India

Kannan, M.; Saranathan, E.; Anbalagan, R.

doi:10.1007/s12517-013-1259-9

Cited by 29 publications

(15 citation statements)

References 19 publications

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“…In the past and recently, there are many quantitative approaches, i.e., statistical and GIS-based integrated approaches, that have been proposed and applied for landslide hazard zonation or susceptibility mapping in many parts of the Himalayas and hilly areas in India [98][99][100][101][102][103][104][105][106][107][108][109][110][111]. However, very few studies attempted in detail landslide susceptibility or hazard mapping and their validation [96].…”

Section: Literature Review Of Landslide Susceptibility Mapping and As...mentioning

confidence: 99%

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

Batar

Watanabe

2021

IJGI

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The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.

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Section: Literature Review Of Landslide Susceptibility Mapping and As...mentioning

confidence: 99%

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

Batar

Watanabe

2021

IJGI

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“…In the late 1970s, qualitative methods were widely used by researchers. In the last few decades, quantitative methods which are based on the relationship between landslide occurrence and controlling factors become popular (van Westen, Rengers, andSoeters 2003, 2008;Glade and Crozier 2005;Chen and Wang 2014;Kannan, Saranathan, and Anbalagan 2015;Samia et al 2018). Landslide susceptibility is the key component of landslide hazard and risk assessment, and land use planning.…”

Section: Introductionmentioning

confidence: 99%

“…Landslide susceptibility zonation has been carried out widely all around the world to demarcate landslide vulnerable areas using remote sensing and Geographical Information System (Anbalagan et al 2015;Kannan, Saranathan, and Anbalagan 2015). Landslide susceptibility mapping is done by two process namely qualitative (knowledge-driven) and quantitative (statistical) techniques (Kaur et al 2017a).…”

Section: Introductionmentioning

confidence: 99%

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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“…Deforestation and anthropogenic activities, together with the non-sustainable developmental projects and destructive practices, have recently increased the frequency of landslides and mass wasting in the Himalayas and the Western Ghats regions, necessitating predictive and mitigative measures. Number of studies have been reported from various parts of India, using different techniques and approaches, related to the landslide risk reduction by systematic mapping and scientific analysis of landslide susceptible areas (Thampi et al, 1998;Arora et al, 2004;Sarkar and Kanungo, 2004;Saha et al, 2005;Pandey et al, 2007;Vijith and Madhu, 2008;Mathew et al, 2009;Das et al, 2010;Kundu et al, 2013;Kannan et al, 2014;Vijith et al, 2014). Though number of studies have been conducted to identify and map landslide susceptibility zones in these regions, no studies have commented on the suitability of a method and approach, which have been replicated for other regions where similar geoenvironmental conditions exist.…”

Section: Introductionmentioning

confidence: 99%

Determining the Suitability of Two Different Statistical Techniques in Shallow Landslide (Debris Flow) Initiation Susceptibility Assessment in the Western Ghats

Krishnan

Pratheesh

Rejith

et al. 2015

EREM

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In the present study, the Information Value (InfoVal) and the Multiple Logistic Regression (MLR) methods based on bivariate and multivariate statistical analysis have been applied for shallow landslide initiation susceptibility assessment in a selected subwatershed in the Western Ghats, Kerala, India, to determine the suitability of geographical information systems (GIS) assisted statistical landslide susceptibility assessment methods in the data constrained regions. The different landslide conditioning terrain variables considered in the analysis are geomorphology, land use/land cover, soil thickness, slope, aspect, relative relief, plan curvature, profile curvature, drainage density, the distance from drainages, lineament density and distance from lineaments. Landslide Susceptibility Index (LSI) maps were produced by integrating the weighted themes and divided into five landslide susceptibility zones (LSZ) by correlating the LSI with general terrain conditions. The predictive performances of the models were evaluated through success and prediction rate curves. The area under success rate curves (AUC) for InfoVal and MLR generated susceptibility maps shows 84.11% and 68.65%, respectively. The prediction rate curves show good to moderate correlation between the distribution of the validation group of landslides and LSZ maps with AUC values of 0.648 and 0.826 respectively for MLR and InfoVal produced LSZ maps. Considering the best fit and suitability of the models in the study area by quantitative prediction accuracy, LSZ map produced by the InfoVal technique shows higher accuracy, i.e. 82.60%, than the MLR model and is more realistic while compared in the field and is considered as the best suited model for the assessment of landslide susceptibility in areas similar to the study area. The LSZ map produced for the area can be utilised for regional planning and assessment process, by incorporating the generalised rainfall conditions in the area.

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Comparative analysis in GIS-based landslide hazard zonation—a case study in Bodi-Bodimettu Ghat section, Theni District, Tamil Nadu, India

Cited by 29 publications

References 19 publications

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

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

Determining the Suitability of Two Different Statistical Techniques in Shallow Landslide (Debris Flow) Initiation Susceptibility Assessment in the Western Ghats

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