Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Minu Treesa Abraham"'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 14, Iss 6, Pp 1747-1760 (2022)
Debris flows are rapid mass movements with a mixture of rock, soil and water. High-intensity rainfall events have triggered multiple debris flows around the globe, making it an important concern from the disaster management perspective. This study pr
Externí odkaz:
https://doaj.org/article/84adebb51ee14693851e2267624d8f24
Publikováno v:
All Earth, Vol 34, Iss 1, Pp 243-258 (2022)
The increase in population and urbanisation of hilly regions have increased the risk due to landslides. This manuscript presents a data-driven approach with a random forest algorithm to estimate the projected area, length, travel distance, and width
Externí odkaz:
https://doaj.org/article/82357e09cf104387b9b8eafacf337115
Autor:
Minu Treesa Abraham, Neelima Satyam, Nakshatram Shreyas, Biswajeet Pradhan, Samuele Segoni, Khairul Nizam Abdul Maulud, Abdullah M. Alamri
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 12, Iss 1, Pp 540-559 (2021)
This study proposes a regional landslide early warning system for Idukki (India), using a decisional algorithm. The algorithm forecasts the possibility of occurrence of landslide by comparing the rainfall thresholds with the cumulated rainfall values
Externí odkaz:
https://doaj.org/article/c4ee3e87bc88473e99a9799ed9f542e9
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 12, Iss 1, Pp 3381-3408 (2021)
With the increasing computational facilities and data availability, machine learning (ML) models are gaining wide attention in landslide modeling. This study evaluates the effect of spatial resolution and data splitting, using five different ML algor
Externí odkaz:
https://doaj.org/article/f3a52b235e314252a5055961f199a933
Publikováno v:
Land, Vol 10, Iss 9, p 989 (2021)
Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data
Externí odkaz:
https://doaj.org/article/2e35dc9b993e47fe9cc96ffe9260e641
Publikováno v:
Sensors, Vol 20, Iss 9, p 2611 (2020)
In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Develo
Externí odkaz:
https://doaj.org/article/1b08170563e045e0b56f6e6997db928a
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering. 14:1747-1760
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::431c420f7bc91c89497fb630494692a3
https://doi.org/10.1016/j.jrmge.2022.01.004
https://doi.org/10.1016/j.jrmge.2022.01.004
Autor:
Neelima Satyam, Minu Treesa Abraham
In the context of increasing number of landslide disasters, it is important to have efficient Landslide Early Warning Systems (LEWS). LEWS can reduce the risk with sufficient warning time and understanding the hazard and forecasting landslides is an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b65580daa0efb5e1403604c31385653
https://doi.org/10.5194/egusphere-egu23-1591
https://doi.org/10.5194/egusphere-egu23-1591
Publikováno v:
Geosciences Journal. 26:289-301
Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67bf62f96394144ef60476bb175d17a2
https://doi.org/10.1007/s12303-021-0026-2
https://doi.org/10.1007/s12303-021-0026-2
Publikováno v:
Journal of Applied Geophysics. 213:105018
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fe33e79de9d97b61858eec105112f3d
https://doi.org/10.1016/j.jappgeo.2023.105018
https://doi.org/10.1016/j.jappgeo.2023.105018