Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(23), P. 12610 - 12610
Published: Nov. 23, 2023
Landslides
are
one
of
the
most
common
catastrophic
mass
flows
in
mountainous
areas.
The
occurrence
fragmentation
leads
to
evolution
integrity
and
stiffness
sliding
mass.
changes
internal
composition
caused
by
basal
erosion
entrainment
make
dynamic
landslides
more
complex.
To
consider
these
complex
processes,
physics-based
models
often
used
analyze
characteristics
landslides.
However,
proprietary
assumptions
limit
their
application
events.
A
single
model
is
not
competent
for
analysis
with
evolving
characteristics.
In
this
study,
two
effectively
integrated
according
landslide.
effects
also
considered.
maximum
velocity,
accumulation
range,
depth
consistent
field
than
those
model.
Under
terrain
conditions
within
a
few
seconds
triggering
stage,
if
disintegration
advanced
2
s,
impact
area
will
increase
about
3.1%
4.1%,
kinetic
energy
20%.
Simulation
results
indicate
that
landslide
body
significantly
affect
subsequent
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(16), P. 4098 - 4098
Published: Aug. 21, 2023
Earthquake
Disaster
Assessment
(EDA)
plays
a
critical
role
in
earthquake
disaster
prevention,
evacuation,
and
rescue
efforts.
Deep
learning
(DL),
which
boasts
advantages
image
processing,
signal
recognition,
object
detection,
has
facilitated
scientific
research
EDA.
This
paper
analyses
204
articles
through
systematic
literature
review
to
investigate
the
status
quo,
development,
challenges
of
DL
for
The
first
examines
distribution
characteristics
trends
two
categories
EDA
assessment
objects,
including
earthquakes
secondary
disasters
as
buildings,
infrastructure,
areas
physical
objects.
Next,
this
study
application
distribution,
advantages,
disadvantages
three
types
data
(remote
sensing
data,
seismic
social
media
data)
mainly
involved
these
studies.
Furthermore,
identifies
six
commonly
used
models
EDA,
convolutional
neural
network
(CNN),
multi-layer
perceptron
(MLP),
recurrent
(RNN),
generative
adversarial
(GAN),
transfer
(TL),
hybrid
models.
also
systematically
details
at
different
times
(i.e.,
pre-earthquake
stage,
during-earthquake
post-earthquake
multi-stage).
We
find
that
most
extensive
field
involves
using
CNNs
classification
detect
assess
building
damage
resulting
from
earthquakes.
Finally,
discusses
related
training
models,
opportunities
new
sources,
multimodal
DL,
concepts.
provides
valuable
references
scholars
practitioners
fields.
Geology Ecology and Landscapes,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 15
Published: Aug. 28, 2024
Landslides
have
a
profound
impact
on
landscape
geology,
resulting
in
extensive
devastation
and
loss
of
human
lives.
Mapping
landslide
susceptibility
is
crucial
for
effective
land
use
planning
mountainous
country
like
Ethiopia.
This
study
was
conducted
the
upper
Didessa
sub-basin,
southwestern
parts
Ethiopia
using
Geographic
Information
System
(GIS)
multi
criteria
evaluation
(MCE)
technique.
employed
blend
primary
data,
encompassing
field
surveys
interviews
with
experts,
as
well
secondary
data
derived
from
diverse
source,
such
remote
sensing
digital
soil
maps,
geological
maps.
A
total
eleven
critical
factors
were
to
assess
triggers
landslides.
These
include
slope,
aspect,
drainage
density,
topographic
wetness
index
(TWI),
stream
power
(SPI),
ruggedness
(TRI),
hypsometric
integral,
lithology,
cover
(LULC),
texture,
distance
roads.
The
analytical
hierarchy
process
(AHP)
method
used
determine
significance
each
indicator
through
pairwise
comparison
matrix.
area
categorized
into
different
zones
based
landslides,
namely
very
high,
moderate,
low,
low.
Results
revealed
that
cultivated
had
highest
likelihood
experiencing
nine
incidents
out
25,
followed
by
built-up
areas
seven
Conversely,
dense
forests,
sparse
grazing
experienced
lower
Out
11
contributing
24%
surveyed
region
deemed
moderate
susceptibility,
12%
6%
falling
categories
high
respectively.
findings
this
research
provide
important
information
policymakers
develop
efficient
measures
preventing
reducing
risks
