Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(14), P. 3259 - 3259
Published: July 6, 2022
After
the
“5·12”
Wenchuan
earthquake
in
2008,
collapses
and
landslides
have
occurred
continuously,
resulting
accumulation
of
a
large
quantity
loose
sediment
on
slopes
or
gullies,
providing
rich
material
source
reserves
for
occurrence
debris
flow
flash
flood
disasters.
Therefore,
it
is
great
significance
to
build
collapse
landslide
susceptibility
evaluation
model
County
local
disaster
prevention
mitigation.
Taking
as
research
object
according
data
1081
historical
points,
well
natural
environment,
this
paper
first
selects
six
categories
environmental
factors
(13
total)
including
topography
(slope,
aspect,
curvature,
terrain
relief,
TWI),
geological
structure
(lithology,
soil
type,
distance
fault),
meteorology
hydrology
(rainfall,
river),
seismic
impact
(PGA),
ecological
(NDVI),
human
activity
(land
use).
It
then
builds
three
single
models
(LR,
SVM,
RF)
CF-based
hybrid
(CF-LR,
CF-SVM,
CF-RF),
makes
comparative
analysis
accuracy
reliability
models,
thereby
obtaining
optimal
area.
Finally,
study
discusses
contribution
prediction
model.
The
results
show
that
(1)
areas
prone
extremely
high
predicted
by
CF-LR,
RF
CF-RF)
an
area
730.595
km2,
377.521
361.772
372.979
318.631
306.51
respectively,
frequency
ratio
precision
0.916,
0.938,
0.955,
0.956,
0.972,
0.984,
respectively;
(2)
ranking
comprehensive
index
based
confusion
matrix
CF-RF>RF>CF-SVM>CF-LR>SVM>LR
AUC
value
CF-RF>RF>CF-SVM>CF-LR>SVM>LR.
To
certain
extent,
coupling
can
improve
more
over
models.
CF-RF
ranks
highest
all
indexes,
with
POA
257.046
0.946;
(3)
rainfall,
river
are
most
important
factors,
accounting
24.216%,
22.309%,
11.41%,
respectively.
necessary
strengthen
monitoring
mountains
rock
masses
close
rivers
case
rainstorms
county
other
similar
post-earthquake
landslides.
iScience,
Journal Year:
2023,
Volume and Issue:
26(4), P. 106479 - 106479
Published: March 23, 2023
The
frequent
urban
floods
have
seriously
affected
the
regional
sustainable
development
in
recent
years.
It
is
significant
to
understand
characteristics
of
flood
risk
and
reasonably
predict
under
different
land
use
scenarios.
This
study
used
random
forest
multi-criteria
decision
analysis
models
assess
spatiotemporal
Zhengzhou
City,
China,
from
2005
2020,
proposed
a
robust
method
coupling
Bayesian
network
patch-generating
simulation
future
probability.
We
found
that
City
presented
an
upward
trend
its
spatial
pattern
was
"high
middle
low
surrounding
areas".
In
addition,
patterns
scenario
would
be
more
conducive
reducing
risk.
Our
results
can
provide
theoretical
support
for
scientifically
optimizing
improve
management.
Land,
Journal Year:
2023,
Volume and Issue:
12(4), P. 744 - 744
Published: March 25, 2023
A
goal
of
land
change
modelers
should
be
to
communicate
scenarios
future
that
show
the
variety
possible
landscapes
based
on
consequences
management
decisions.
This
study
employs
Markov-FLUS
model
simulate
land-use
changes
in
Hubei
Province
multiple
consider
social,
economic,
and
ecological
policies
using
18
driving
factors,
including
point-of-interest
data.
First,
was
developed
validated
with
historical
data
from
2000
2020.
The
then
used
2020
2035
four
scenarios:
natural
development,
economic
priority,
protection,
cultivated
protection.
results
effectively
simulates
pattern
Province,
an
overall
accuracy
0.93
for
use
simulation
Kappa
coefficient
FOM
index
also
achieved
0.86
0.139,
respectively.
In
all
scenarios,
remained
primary
type
2035,
while
construction
showed
increasing
trend.
However,
there
were
large
differences
simulated
patterns
different
scenarios.
Construction
expanded
most
rapidly
priority
scenario,
it
more
slowly
protection
scenario.
We
designed
scenario
restrict
rapid
expansion
land.
development
encroached
forests.
contrast,
forests
water
areas
well-preserved,
decrease
increase
suppressed,
resulting
a
improvement
sustainability.
Finally,
spread
curbed.
conclusion,
applied
this
has
substantial
implications
effective
utilization
resources
environment
Province.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(2), P. 336 - 336
Published: Jan. 15, 2024
Flooding
is
a
natural
disaster
that
coexists
with
human
beings
and
causes
severe
loss
of
life
property
worldwide.
Although
numerous
studies
for
flood
susceptibility
modelling
have
been
introduced,
notable
gap
has
the
overlooked
or
reduced
consideration
uncertainty
in
accuracy
produced
maps.
Challenges
such
as
limited
data,
due
to
confidence
bounds,
overfitting
problem
are
critical
areas
improving
accurate
models.
We
focus
on
mapping,
mainly
when
there
significant
variation
predictive
relevance
predictor
factors.
It
also
noted
receiver
operating
characteristic
(ROC)
curve
may
not
accurately
depict
sensitivity
resulting
map
overfitting.
Therefore,
reducing
was
targeted
increase
improve
processing
time
prediction.
This
study
created
spatial
repository
test
models,
containing
data
from
historical
flooding
twelve
topographic
geo-environmental
conditioning
variables.
Then,
we
applied
random
forest
(RF)
extreme
gradient
boosting
(XGB)
algorithms
susceptibility,
incorporating
variable
drop-off
empirical
loop
function.
The
results
showed
function
crucial
method
resolve
model
associated
factors
methods.
approximately
8.42%
9.89%
Marib
City
9.93%
15.69%
Shibam
were
highly
vulnerable
floods.
Furthermore,
this
significantly
contributes
worldwide
endeavors
focused
hazards
linked
disasters.
approaches
used
can
offer
valuable
insights
strategies
risks,
particularly
Yemen.
