Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-07

AUTHORS

Iswar Das, Gaurav Kumar, Alfred Stein, Arunabha Bagchi, Vinay K. Dadhwal

ABSTRACT

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment. More... »

PAGES

25-37

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10661-010-1668-0

DOI

http://dx.doi.org/10.1007/s10661-010-1668-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050660192

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20809386


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