Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

Pradhan, Biswajeet; Mansor, Shattri; Pirasteh, Saied; Buchroithner, Manfred

doi:10.1080/01431161.2010.484433

Cited by 137 publications

(63 citation statements)

References 38 publications

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“…According to Pradhan et al (2011), RS techniques, such as satellite imagery, assist to detect landslides in a cost-and time-effective way. The data used in this study for analysis were gathered in two different days.…”

Section: Data Usedmentioning

confidence: 99%

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Siyahghalati

Saraf

Pradhan

et al. 2014

Geomatics, Natural Hazards and Risk

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Landslide is considered as one of the most devastating and most costly natural hazards in highlands, which is triggered mainly by rainfalls or earthquakes. In comparison with other methods, landslide mapping and monitoring via remote sensing data products are considered as the least expensive method of data collection. The current research attempts to detect landslides which occurred due to a 6.9 magnitude earthquake in Sikkim Himalaya, India, on 18 September 2011 and also to establish the spatial relationship between landslides and the slope of the terrain. To detect the landslides, decision tree method was applied on two Indian remote sensing satellites linear imaging self-scanning sensor (LISS III) images acquired from 2007 and 2011 which were taken before and after the earthquake. As the study area was relatively huge for identifying the landslides, the region was separated into two parts: "tested study area" and "real study area". The overall accuracy of landslide detection was 76%, and 75% for tested and real study area, respectively. Then, the spatial relationship between the landslides and the slope of the terrain was conducted using the digital elevation model. The results revealed that most of the landslides occurred between the slope of 25 and 45 covering 2.3 km 2 and no landslide recorded in the slope of 65 -90 in the real study area. The results obtained in this study may be useful for decision-making and policy support towards reconstruction effort after the landslide occurrence. In addition, the information can be useful for reducing the risk of potential damages to substructures and properties by developing new and efficient strategies.

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Section: Data Usedmentioning

confidence: 99%

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Siyahghalati

Saraf

Pradhan

et al. 2014

Geomatics, Natural Hazards and Risk

Self Cite

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“…According to the geological hazard, these models have been used to analyze landslide susceptibility mapping in many recent studies [17][18][19][20][21][22][23][24][25][26][27]. In this study, the fuzzy algebra model was used to integrate ground subsidence hazard maps, withthis model being used most frequently in analysis landslide susceptibility mapping [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of ground subsidence hazard maps using fuzzy logic

Park

Lee

Lee³

2014

Open Geosciences

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Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping groundsubsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

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“…They include logistic regression [6,7,13,15,37], frequency ratio [27,29,38], weights of evidence [3,8,35,39], Dempster-Shafer [27,34], support vector machine [36,44], Bayesian network [12].…”

Section: Introductionmentioning

confidence: 99%

A case study for the integration of predictive mineral potential maps

Lee

Heo

et al. 2014

Open Geosciences

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Abstract:This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) -silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development. Keywords

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Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

Cited by 137 publications

References 38 publications

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Ensemble of ground subsidence hazard maps using fuzzy logic

A case study for the integration of predictive mineral potential maps

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