Earthquake hazard assessment through geospatial model and development of EaHaAsTo tool for visualization: an integrated geological and geoinformatics approach

Sivakumar, R.; Ghosh, Snehasish

doi:10.1007/s12665-017-6777-4

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…Soils of the Namchi region are especially composed mainly Loamy soil group, that is classify Loamy Skeletal Weak Soil, Shallow Depth Soil, Loamy Skeletal Weathered Soil, Coarse Loamy Rock soil, Fine Loamy Mixed Soil, Coarse Loamy excessive drainage rock valley fill. In the study area, it is found that Loamy Skeletal Weak Soil and Loamy Skeletal Weathered Soil fall in south east part, Fine Loamy Mixed Soil and Coarse Loamy soil take a look at in north west part, in that area observe as a high landslide area (Sivakumar, 2017). (Figure 8) (Lallianthanga, 2013).…”

Section: Soil Mapmentioning

confidence: 84%

“…The influences of the fundamental soils at the avalanche shaking are extra significant. Seismologists have discovered that the solid seismic shaking is over and over situated in the coarse loamy powerless and liquefied soil (Sivakumar, 2017). The landslide incidence chance cost of rock sandy loam soil and also sandy loam are higher and lesser in exceptional sandy loam, silt loam, gravelly silt loam and loam.…”

Section: Soil Mapmentioning

confidence: 99%

“…Movement of the fault because of tectonic action mains to alteration the slope representative excessive probability of landslide. In the northern a part of the have a observe area, much less frequency and significance of landslide became distributed within the moderate and very moderate slope location, signifying the location is seismically much a reduced amount of active (Sivakumar, 2017). (Figure 9)…”

Section: Soil Mapmentioning

confidence: 99%

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Slope Studies Mapping of Landslide Hazard: A Study on Namchi, Sikkim Using Gis

CHANDRA,

SINGH

2024

IJSR

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This paper presents a case study of slope studies in Namchi, Sikkim, India, using Geographic Information System (GIS) to map landslide hazards. The GIS analysis was used to identify areas of steep slopes that have high potential for slope instability and thus landslide hazard. The results of the study showed that the highest landslide hazard was located in the areas of steep slopes and along the river channels. The study also found that the areas of high landslide hazard were also areas of high land cover change. As per the Centre for Research at the Epidemiology of Disasters (CRED), landslide positioned fifth in expressions of number of passing is out of top ten biggest significant disasters. Inside Himalaya district, Sikkim Darjeeling Himalayas is one of the best landslide inclined regions. AHP is widely used in site selection and suitability, habitat suitability analysis, and LHZ and mapping. A formal land use land cover classification was prepared using Arc GIS. A soil map was available from DRDO database. Geology and lithology map were given by DRDO database and weightage was given according to the importance. A layer of relief including slope and degree of aspect were included in the study. This research at is an attempt on this way and attempts to investigate the components assuming principle role inside the occurrence of landslide in this area.

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Section: Soil Mapmentioning

confidence: 84%

Section: Soil Mapmentioning

confidence: 99%

Section: Soil Mapmentioning

confidence: 99%

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Slope Studies Mapping of Landslide Hazard: A Study on Namchi, Sikkim Using Gis

CHANDRA,

SINGH

2024

IJSR

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“…-78 A/12 (1932) on a 1:63,360 scale was collected from the historical maps of South Asia, due to the unavailability of this toposheet in the Survey of India Open Data Archive. The geology map and the soil properties map for the Ranikhola watershed were prepared from the Geology and Soil Map of Sikkim on a 1.100000 scale (CISMHE 2007;Sivakumar and Ghosh 2017). Gridded rainfall data (0.25°×0.25°, daily) for the period from 1990 to 2020 was acquired from the India Meteorological Department (IMD).…”

Section: Datasetsmentioning

confidence: 99%

An Assessment of Flash Flood Vulnerable Zones Using Integrated Grey Fuzzy Analytic Hierarchy Process-based Model for a Himalayan Watershed

roy

Dhar

Desai

2022

Preprint

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Flash flood is the most recurrent natural threat in the northeastern part of India, especially during the peak of the monsoon season. Considering the exponential rise in the frequency of flash flood events, identifying the Flash Flood Vulnerable Zones (FFVZs) is one of the most crucial findings to limit their negative consequences. In this study, Grey Fuzzy Analytic Hierarchy Process (GFAHP) based model integrated with the Geographic Information System (GIS) is implemented to assess the flash flood vulnerability in the Ranikhola watershed, East Sikkim, India. The GFAHP combines the benefits of all the traditional Multi-Criteria Decision-Making (MCDM) approaches. The Fuzzy Analytic Hierarchy Process (FAHP) is used to compute the weights, while the weight coefficients are evaluated using the Grey method. A novel Flash Flood Vulnerability Index (FFVI) is proposed to assess the flash flood vulnerability that takes into account twelve natural and anthropogenic parameters. Further, the FFVI map is classified into three FFVZs: low, moderate, and high. The efficacy of the flash flood vulnerability map is calculated by a single effectiveness value (EV), and the result is between moderate and more significant. The sensitivity analysis results show the influence of individual attributes on FFVI and FFVZs.

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“…Models based on measurement of elastic and electromagnetic waves to predict earthquakes and tsunami was done [15]. Earthquake hazard assessment was done using EaHaAsTo tool for visualization [16]. Determined the threshold energy leading to seismic activity [17].…”

Section: Introductionmentioning

confidence: 99%

Earthquake trend prediction using long short-term memory RNN

Bhandarkar

Vardaan

Satish

et al. 2019

IJECE

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<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>

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Earthquake hazard assessment through geospatial model and development of EaHaAsTo tool for visualization: an integrated geological and geoinformatics approach

Cited by 13 publications

References 29 publications

Slope Studies Mapping of Landslide Hazard: A Study on Namchi, Sikkim Using Gis

Slope Studies Mapping of Landslide Hazard: A Study on Namchi, Sikkim Using Gis

An Assessment of Flash Flood Vulnerable Zones Using Integrated Grey Fuzzy Analytic Hierarchy Process-based Model for a Himalayan Watershed

Earthquake trend prediction using long short-term memory RNN

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