Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India)

Rautela, Piyoosh; Lakhera, R. C.

doi:10.1016/s0303-2434(00)85009-6

Cited by 81 publications

(37 citation statements)

References 5 publications

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“…Recently, there were studies on landslide hazard evaluation using GIS, and many of these studies have applied probabilistic methods (Rowbotham and Dudycha 1998;Guzzetti et al 1999;Jibson et al 2000;Luzi et al 2000;Parise and Jibson 2000;Rautelal and Lakhera 2000;Baeza and Corominas 2001;Lee and Min 2001;Temesgen et al 2001;Clerici et al 2002;Donati and Turrini 2002;Lee et al 2002a,b;Rece and Capolongo 2002;Zhou et al 2002;Chung and Fabbri 2003;Remondo et al 2003;Lee and Choi 2003c;Lee et al 2004b). The logistic regression method has also been applied to landslide hazard mapping (Atkinson and Massari 1998;Dai et al 2001;Dai and Lee 2002;Ohlmacher and Davis 2003).…”

Section: Introductionmentioning

confidence: 99%

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

2006

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The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model. Keywords Landslide . Frequency ratio . Logistic regression . GIS . Remote sensing Introduction Globally, landslides cause approximately 1,000 deaths per year and property damage of about US 4 billion. Recently in Malaysia, landslides have posed serious threats to settlements and structures that support transportation, natural resource management and tourism. They cause considerable damage to highways, waterways and pipelines. Most of these landslides occurred on cut slopes or embankments alongside roads and highways in mountainous areas. Few landslides occurred near high-rise apartments and in residential areas, causing death to human beings. The recent landslides that occurred near the North Klang Valley Express Way are good examples in Malaysia.In tropical countries like Malaysia, most landslides are triggered by heavy rainfall. In literature, many attempts have been made to predict these landslides and prepare susceptibility maps using different methods. In this paper, remote sensing data along with other tabular information were used to prepare landslide hazard map for Selangor area. Terrain information such as slope, aspect, curvature, distance from drainage, geology, distance from lineament, soil, land cover, normalized difference vegetation index (NDVI), and precipitation were used as landslide causative parameters. Landslide hazard map was prepared using frequency ratio and logistic regression models. Landslide hazard analyzes were carried out using the map overlay technique in GIS.Recently, there were studies on landslide hazard evaluation using GIS, and many of these studies have applied probabilistic methods (

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Section: Introductionmentioning

confidence: 99%

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

2006

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“…In this method, a weight value for a parameter class is defined as the natural logarithm of the landslide density class, divided by the landslide density in the entire map (Van Westen 1997;Rautela and Lakhera 2000). The formula given below forms the basis of the approach: where Wi = the weight given to a certain parameter class, Densclass = landslide density within the parameter class, Densmap = landslide density within the entire map, Npix (Si) = number of pixels that contain landslides in a certain parameter class, and Npix (Ni) = total number of pixels in a certain parameter class.…”

Section: Statistical Index (Wi) Methodsmentioning

confidence: 99%

“…The weighting factor values may be selected either arbitrarily mainly on the basis of expert opinion (Anbalagan 1992;Turrini and Visintainer 1998) or through some intermediate processes (Rautela and Lakhera 2000;Lee and Min 2001;Dai et al 2001). The weighting factor (Wf) method used in this study is a modification of Wi method.…”

Section: Weighting Factor (Wf) Methodsmentioning

confidence: 99%

Relocation of a Problematic Segment of a Natural Gas Pipeline Using GIS-Based Landslide Susceptibility Mapping, Hendek (Turkey)

Boukpeti¹,

Charlier

Hueckel³

et al. 2004

Engineering Geology for Infrastructure Planning in Europe

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Abstract.A segment of natural gas pipeline constructed in 1997 to supply natural gas to a steel factory was broken due to a landslide with fire near Hendek, Turkey. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map of the corresponding segment. In this study, statistical methods namely statistical index (Wi) and weighting factor (WF) have been used with geographic information systems (GIS) by analyzing several intrinsic factors controlling the landslides to prepare landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides mainly occur in the unconsolidated to semi-consolidated clayey unit and regolith. Lithology, land use/land cover, elevation, slope, and distance to stream are found to be the important parameters for the study area whereas aspect is not. Nevertheless, the drainage density has a very low contribution. Based on the findings obtained in this study, an alternative route to be studied for detailed engineering geological investigations is proposed.

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“…Studies around the world focus on mapping landslide susceptibility based on combining landslide inventories with probabilistic, deterministic, and statistical approaches [8][9][10][11][12][13]. Physically-based (deterministic) models are widely used and have shown good results for shallow slope stability around the world.…”

Section: Introductionmentioning

confidence: 99%

Determination of Areas Susceptible to Landsliding Using Spatial Patterns of Rainfall from Tropical Rainfall Measuring Mission Data, Rio de Janeiro, Brazil

Guimarães

Machado

Júnior

et al. 2017

IJGI

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Spatial patterns of shallow landslide initiation reflect both spatial patterns of heavy rainfall and areas susceptible to mass movements. We determine the areas most susceptible to shallow landslide occurrence through the calculation of critical soil cohesion and spatial patterns of rainfall derived from TRMM (Tropical Rainfall Measuring Mission) data for Paraty County, State of Rio de Janeiro, Brazil. Our methodology involved: (a) creating the digital elevation model (DEM) and deriving attributes such as slope and contributing area; (b) incorporating spatial patterns of rainfall derived from TRMM into the shallow slope stability model SHALSTAB; and (c) quantitative assessment of the correspondence of mapped landslide scars to areas predicted to be most prone to shallow landsliding. We found that around 70% of the landslide scars occurred in less than 10% of the study area identified as potentially unstable. The greatest concentration of landslides occurred in areas where the root strength of vegetation is an important contribution to slope stability in regions of orographically-enhanced rainfall on the coastal topographic flank. This approach helps quantify landslide hazards in areas with similar geomorphological characteristics, but different spatial patterns of rainfall.

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Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India)

Cited by 81 publications

References 5 publications

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

Relocation of a Problematic Segment of a Natural Gas Pipeline Using GIS-Based Landslide Susceptibility Mapping, Hendek (Turkey)

Determination of Areas Susceptible to Landsliding Using Spatial Patterns of Rainfall from Tropical Rainfall Measuring Mission Data, Rio de Janeiro, Brazil

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