Remote sensing GIS-based landslide susceptibility &amp; risk modeling in Darjeeling–Sikkim Himalaya together with FEM-based slope stability analysis of the terrain

Nath, Sankar Kumar; Sengupta, Arnab; Srivastava, Anand

doi:10.1007/s11069-021-04823-5

Cited by 22 publications

(7 citation statements)

References 83 publications

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“…The predicted probabilistic LPI distribution for a 475 year return period categorizes the region into four zones: "low (LPI=0)" in Leh, Shimla, Dehradun, Thimphu, Gangtok, Shillong, Aizawl, Kohima and Bhubaneswar; "moderate (0 < LPI ≤ 5)" in Jammu, New Delhi and Varanasi; "high (5 < LPI ≤ 15)" in Chandigarh, Prayagraj and Kolkata; and "severe (LPI > 15)" in Srinagar, Amritsar, Lucknow, Patna, Kathmandu, Dhaka, Chittagong, Guwahati, Imphal and Agartala. (Sengupta and Nath 2024;Sengupta et al 2020;Nath et al 2021b).…”

Section: Guwahati In Assam and (H) At Thimphu In Bhutanmentioning

confidence: 99%

“…24 Spatial distribution of Liquefaction Potential Index in the present Tectonic Ensemble for the Surfaceconsistent Probabilistic scenario for 10% probability of exceedance in 50 years Additionally, Landslide Susceptibility Zonation (LSZ) has been accomplished in the present study by integrating different causative factors viz. Surface Geology, Lineament Density, Landform, Elevation, Slope Angle, Slope Aspect, Drainage Density, Normalized Differences Vegetation Index (NDVI), Landuse/landcover (LULC), Distance to road, Rainfall, Epicentre Proximity and Surface-consistent Peak Ground Acceleration (PGA) with 10% probability of exceedance in 50 years with a return period of 475 years as thematic layers on GIS platform through Logistic Regression (LR) technique(Sengupta and Nath 2024;Sengupta et al 2020;Nath et al 2021b).…”

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confidence: 99%

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Site-characterization using 1D/2D/3D Site Response Modelling vis-à-vis Surface-consistent Probabilistic Seismic Hazard, Microzonation and Damage Potential Studies in the Six-Tectonic Ensemble consisting of Kashmir Himalaya to Northeast India

Nath,

Biswas,

Srivast

et al. 2024

Preprint

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Indian subcontinent, marked by its intricate seismotectonic settings stands out as the most active region in the globe. This study conducts a firm rock-compliant Probabilistic Seismic Hazard Assessment considering both tectonic and polygonal sources in 0-25km, 25-70km, 70-180km, and 180-300km depth ranges, Next Generation Attenuation models through a logic tree framework across the Tectonic Ensemble from Kashmir Himalaya to Northeast India encompassing six seismogenic tectonic provinces classifying the Ensemble in the 'High' to 'Severe' hazard regime, highlighting the need for a site-specific surface-consistent investigation that shear-wave velocity characterises the Ensemble into 11 site classes and estimates Surface-consistent Probabilistic Seismic Hazard of the Ensemble in the range of 0.25-2.91g for 475 years of return period using a systematic 1D/2D/3D site response analysis. Liquefaction Potential and Landslide Susceptibility Index adds secondary hazard to both geohazard and seismic hazard regime for a comprehensive seismic hazard microzonation of the important Cities and urban centres in the Tectonic Ensemble. The damage states modelled using capacity spectrum method on prevailing building types for the Surface-consistent Probabilistic PGA by SELENA are found to be well-aligned with the reported damage scenarios of A1, URM, and RC buildings triggered by large historical earthquakes in each of the tectonic provinces.

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Section: Guwahati In Assam and (H) At Thimphu In Bhutanmentioning

confidence: 99%

mentioning

confidence: 99%

Site-characterization using 1D/2D/3D Site Response Modelling vis-à-vis Surface-consistent Probabilistic Seismic Hazard, Microzonation and Damage Potential Studies in the Six-Tectonic Ensemble consisting of Kashmir Himalaya to Northeast India

Nath,

Biswas,

Srivast

et al. 2024

Preprint

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“…A random forest (RF) is an ensemble learning method used for solving classification, regression problems, and other tasks. During training, the RF builds a large number of DTs, which are then used to make predictions [86,87]. In classification tasks, the RF output is the class selected by most of the DTs, while in regression problems, the mean or average prediction value from each DT is returned as the RF result.…”

Section: Decision Tree (Dt) Random Forest (Rf) and Gradient Boosting ...mentioning

confidence: 99%

Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Xu,

He,

Shan

et al. 2023

Modelling

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In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.

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“…Guwahati (Nath et al, 2007), the Sikkim Himalaya (Nath, 2004), Chennai (Ganapathy, 2011), Kolkata (Nath et al, 2014), Kachchh (Pancholi et al, 2022) etc. There are multivariate statistical approaches like logistic regression (LR) proposed by Althuwaynee et al (2014) and Hemasinghe et al (2018) which have found wider applications in similar multi-criterion integration framework used in landslide hazard zonation alongside machine learning data-driven Random Forest (RF) technique, another most popular ensemble learning method, developed by Breiman (2001) and used successfully by Nath et al (2021b) in the multi-criterion landslide susceptibility mapping in Darjeeling-Sikkim Himalaya employing all of AHP, LR and RF techniques mimicking the hazard zonation within 87% confidence bound. In the present study, we limit ourselves to AHP hierarchical structure that quantifies relative importance for a given set of themes on a ratio scale depending on the user's judgment.…”

Section: Holistic Seismic Hazard Microzonation Of the Cities Of Dhanb...mentioning

confidence: 99%

Site-specific seismic hazard and risk potential of Bengal Basin with emphasis on holistic seismic hazard microzonation and its structural impact assessment in the cities of Dhanbad and Mymensingh

et al. 2022

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The Bengal Basin located in the eastern part of the Indian subcontinent at the conjunction of the Eurasian, Indian, and Indo-Burma plates with two progressing deformation fronts viz. the Himalayas and the Indo-Burmese orogenic belts is one of the largest fluvio-deltaic to shallow marine sedimentary basin covered by alluvial plains of Holocene deposits extending from the Himalayas to the Bay of Bengal over thick younger alluvium comprising shallow layers of silt, clay, and sand that can have disastrous consequences due to site-specific ground motion amplification and liquefaction effects. The basin surrounded by Shillong and Assam plateaus in the Northeast is in the active tectonofabric of major active faults and lineaments triggering many devastating earthquakes in the past implicating the MM Intensity of VIII–XI in the near-source region causing widespread damage and destruction in the basin, thus bringing in the essence of assessing surface level seismic hazard and the risk imposed on the basin. Consideration of seismicity patterns, fault networks, and similarity in focal mechanisms yielded 49 areal seismogenic sources and additional active tectonic features in the 0–25 km, 25–70 km, and 70–180 km hypocentral depth ranges, which along with 14 ground motion prediction equations that include site-specific next generation spectral attenuation models pertaining to Northeast India, East-Central Himalaya, and Bengal Basin tectonic provinces yielded probabilistic peak ground acceleration (PGA) at engineering bedrock in the range of 0.08–0.58 g. Both the geophysical and geotechnical investigations at 6,000 sites provided effective shear wave velocity distribution in the range of 113–948 m/s on the geographical information system, thus classifying the basin into 11 site classes with “None” to “Severe” liquefaction hazard potential. A systematic non-linear/equivalent linear site response analysis and its spectral convolution with firm rock peak ground acceleration yielded surface-consistent hazard in the range of 0.09–1.17 g, thus opening up the issue of risk assessment and holistic seismic hazard microzonation of all the cities in the basin and their structural impact assessment using the SELENA-based capacity spectrum method on FEMA and BMTPC-regulated 11 model building types in the damage states of “none,” “slight,” “moderate,” “extensive,” and “complete” for all of those, however, in-depth studies carried out for Mymensingh and Dhanbad have been presented.

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Remote sensing GIS-based landslide susceptibility & risk modeling in Darjeeling–Sikkim Himalaya together with FEM-based slope stability analysis of the terrain

Cited by 22 publications

References 83 publications

Site-characterization using 1D/2D/3D Site Response Modelling vis-à-vis Surface-consistent Probabilistic Seismic Hazard, Microzonation and Damage Potential Studies in the Six-Tectonic Ensemble consisting of Kashmir Himalaya to Northeast India

Site-characterization using 1D/2D/3D Site Response Modelling vis-à-vis Surface-consistent Probabilistic Seismic Hazard, Microzonation and Damage Potential Studies in the Six-Tectonic Ensemble consisting of Kashmir Himalaya to Northeast India

Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Site-specific seismic hazard and risk potential of Bengal Basin with emphasis on holistic seismic hazard microzonation and its structural impact assessment in the cities of Dhanbad and Mymensingh

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