GIS-Based Landslide Susceptibility Mapping in Eastern Boundary Zone of Northeast India in Compliance with Indo-Burmese Subduction Tectonics

Sengupta, Arnab; Nath, Sankar Kumar

doi:10.1007/978-3-030-75197-5_2

Cited by 9 publications

(6 citation statements)

References 44 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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“…Terrain alteration may impact a region depending on its distance from a stream (Hua et al, 2021). Drainage proximity is a buffer distance from the center of river/stream line (Pradhan and Kim, 2020;Sengupta and Nath, 2022). Drainage proximity is divided into five classes: <50, 50-100, 100-200, 200-500 and >500m, as shown in Figure 4a.…”

Section: Soft Computing Machine Learning Applicationsmentioning

confidence: 99%

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Manandhar,

Huynh,

Bhattarai

et al. 2024

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PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

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“…Several validation model evaluation methodologies, such as sensitivity, receiver operating characteristics (ROC), specificity, area under curve (AUC), and accuracy, were used in this work to validate the performance of the prediction model. The effectiveness of landslide prediction has recently been extensively assessed using the ROC curves method [58,59]. The inputs used to plot the ROC curve were true positive, which stands for a properly anticipated landslide on the X-axis, and false positive, which stands for an incorrectly predicted landslide on the Y-axis.…”

Section: Performance Evaluation Of the Modelmentioning

confidence: 99%

Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India

Saha

Villuri

Bhardwaj

2022

Land

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Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses.

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GIS-Based Landslide Susceptibility Mapping in Eastern Boundary Zone of Northeast India in Compliance with Indo-Burmese Subduction Tectonics

Cited by 9 publications

References 44 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

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India

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