http://www.cnr.it/ontology/cnr/individuo/prodotto/ID273180
A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy (Abstract in rivista)
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- Label
- A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy (Abstract in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Alternative label
Taylor F. E. (1), Santangelo M. (2,3), Marchesini I. (2), and Malamud B. D. (1) (2013)
A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy
(literal)
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- Taylor F. E. (1), Santangelo M. (2,3), Marchesini I. (2), and Malamud B. D. (1) (literal)
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- (1) Earth and Environmental Dynamics Research Group, Department of Geography, King's College London, Strand, London, WC2R 2LS, United Kingdom
(2) CNR-IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy,
(3) Dipartimento di Scienze della Terra, Università degli Studi di Perugia, Piazza dell'Università 1, 06100 Perugia, Italy (literal)
- Titolo
- A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy (literal)
- Abstract
- During a landslide triggering event, the tens to thousands of landslides resulting from the trigger (e.g., earthquake,
heavy rainfall) may block a number of sections of the road network, posing a risk to rescue efforts, logistics and
accessibility to a region. Here, we present initial results from a semi-stochastic model we are developing to evaluate
the probability of landslides intersecting a road network and the network-accessibility implications of this across
a region. This was performed in the open source GRASS GIS software, where we took 'model' landslides and
dropped them on a 79 km2 test area region in Collazzone, Umbria, Central Italy, with a given road network (major
and minor roads, 404 km in length) and already determined landslide susceptibilities. Landslide areas (AL ) were
randomly selected from a three-parameter inverse gamma probability density function, consisting of a power-law
decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL ; the
rollover (maximum probability) occurs at about AL = 400 m2 The number of landslide areas selected for each
.
triggered event iteration was chosen to have an average density of 1 landslide km-2 , i.e. 79 landslide areas chosen
randomly for each iteration. Landslides were then 'dropped' over the region semi-stochastically: (i) random points
were generated across the study region; (ii) based on the landslide susceptibility map, points were accepted/rejected
based on the probability of a landslide occurring at that location. After a point was accepted, it was assigned a
landslide area (AL ) and length to width ratio. Landslide intersections with roads were then assessed and indices
such as the location, number and size of road blockage recorded. The GRASS-GIS model was performed 1000
times in a Monte-Carlo type simulation. Initial results show that for a landslide triggering event of 1 landslide
km-2 over a 79 km2 region with 404 km of road, the number of road blockages ranges from 6 to 17, resulting in
one road blockage every 24-67 km of roads. The average length of road blocked was 33 m. As we progress with
model development and more sophisticated network analysis, we believe this semi-stochastic modelling approach
will aid civil protection agencies to get a rough idea for the probability of road network potential damage (road
block number and extent) as the result of different magnitude landslide triggering event scenarios. (literal)
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