Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

Meena, Sansar Raj; Ghorbanzadeh, Omid; Westen, C.J. van; Nachappa, Thimmaiah Gudiyangada; Blaschke, Thomas; Singh, Ramesh P.; Sarkar, Raju

doi:10.1007/s10346-020-01602-4

Cited by 72 publications

(23 citation statements)

References 45 publications

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“…Осим становништва у насељеним областима, богатство флоре и фауне на овом подручју може бити значајно угрожено природним и антропогеним процесима. Осим наведеног, ове непогоде значајно утичу и на привредне делатности и инфраструктуру (Dai et al, 2002;Tošić et al, 2014;Hao et al, 2020;Meena et al, 2021;Morar et al, 2021).…”

Section: Discussionunclassified

“…Apart from the population in populated areas, the richness of flora and fauna in this area can be significantly endangered by natural and anthropogenic processes. In addition to the above, these disasters significantly affect economic activities and infrastructure (Dai et al, 2002;Tošić et al, 2014;Hao et al, 2020;Meena et al, 2021;Morar et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Durlević

2021

Glas Srp geogr drus

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Torrential floods and landslides are frequent natural disasters in Serbia, but also in the Mlava River Basin. Due to the large number of settlements, the main goal of this research is to determine the locations that are most susceptible to torrential floods and landslides in the Mlava River Basin. Using geographic information systems (GIS), the first step is the analysis the susceptibility of the terrain to torrential floods using the Flash Flood Potential Index (FFPI) method. According to the obtained data, it was determined that 31.53% of the Mlava River Basin is susceptible, and 10.46% is very susceptible to torrential floods. The second step is the analysis of the susceptibility of the terrain to landslides, for which the statistical Probability method (PM) and the Landslide Susceptibility Index (LSI) were used. According to the results of the LSI index and PM method, 8.09% and 14.04% of the basin area is in the category of high and very high susceptibility to landslides. This paper represents a significant step towards a better understanding of unfavorable natural conditions in the Mlava River Basin, and the obtained results are applicable to numerous human activities in the research area (environmental protection, sustainable management of agricultural plots, protection of water and forest resources and ecosystems, etc.).

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Section: Discussionunclassified

Section: Introductionmentioning

confidence: 99%

Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Durlević

2021

Glas Srp geogr drus

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“…MCC was used to evaluate the cells in the ETL prediction maps relative to the reference landslide map. Several studies (Rong et al, 2020;Wang et al, 2020;Meena et al, 2021) have adopted MCC to evaluate landslide susceptibility maps. Rong et al (2020) obtained MCC 0.44 for a hazard map of rainfall-induced landslides in Shuicheng, China.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

et al. 2021

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Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.

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“…Globally, the situations are very similar (e.g. Kanungo et al 2008;Lee et al 2016;Lazzari and Piccarreta 2018;Xiao et al 2020;Wang et al 2020;Thakur et al 2020;Mallick et al 2021;Meena et al 2021). In reality, the exposure level of the AH-48 assets is highly vulnerable due to recurrent landslide phenomenon that cause continuous damage to the structures of the highway.…”

Section: Geohazards Along the Asian Highway Ah-48mentioning

confidence: 99%

Geohazard vulnerability and condition assessment of the Asian highway AH-48 in Bhutan

Tempa

Chettri

Aryal³

et al. 2021

Geomatics, Natural Hazards and Risk

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Transport networks are frequently exposed to geohazards, and losses can be observed frequently by notable disaster incidents such as landslides, earthquakes, and floods. Although losses due to natural disasters are frequently reported, limited attention is paid to assess the condition of strategic road networks in the Himalayas. Thus, to fulfill this gap, this study performs condition assessment of Asian Highway AH-48 in Bhutan and develops a system to rate the performance level of road assets. The assessment is conducted along a 157.6 km stretch of AH-48. The sum of findings highlights that about 55% of the road assets along the AH-48 are highly vulnerable to geohazards. Drains are observed to be the most vulnerable, with 52% drains lying below the standard rating among the considered assets followed by retaining walls and pavement. Landslide vulnerability function for highways that are situated in mountain regions is developed using the newly proposed landslide intensity scale. The sum of findings highlights that the Himalayan roads will be affected mostly by landslides and the drainage system is the most affected road asset showing the least serviceability among the common road assets.

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Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

Cited by 72 publications

References 45 publications

Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

Geohazard vulnerability and condition assessment of the Asian highway AH-48 in Bhutan

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