Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
162, P. 112000 - 112000
Published: April 13, 2024
Climate
change
has
significantly
increased
the
risks
associated
with
urban
flooding.
However,
most
research
on
flood
risk
assessment
focuses
large-scale
climate
changes
and
impacts,
leaving
a
gap
in
high
spatial
resolution
of
inter-urban
areas.
This
makes
it
difficult
to
guide
regional
planning
for
government.
Therefore,
this
study
aims
explore
floods
ultra-high-density
cities
under
at
scale,
using
Hong
Kong
as
case
study.
We
comprehensively
assessed
index
(FRI)
built
environment
211
tertiary
units
(TPUs)
from
three
dimensions
vulnerability,
exposure,
hazard
2006
2021.
also
employed
prediction
model
forecast
spatial–temporal
patterns
FRI
next
5,
10,
15
years
evaluated
uneven
distribution
risks.
The
results
show
that
TPUs
yearly,
which
poses
higher
threats
agglomerative
areas
transportation
functional
facilities.
Additionally,
future
will
further
impact
coastal
western
Kong,
resulting
more
negative
impacts
high-building
should
prioritize
integrating
management
mitigation
measures.
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.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(2), P. 350 - 350
Published: Jan. 16, 2024
Coastal
regions,
increasingly
threatened
by
floods
due
to
climate-change-driven
extreme
weather,
lack
a
comprehensive
study
that
integrates
coastal
and
riverine
flood
dynamics.
In
response
this
research
gap,
we
conducted
bibliometric
analysis
thorough
visualization
mapping
of
studies
compound
flooding
risk
in
cities
over
the
period
2014–2022,
using
VOSviewer
CiteSpace
analyze
407
publications
Web
Science
Core
Collection
database.
The
analytical
results
reveal
two
persistent
topics:
way
explore
return
periods
or
joint
probabilities
drivers
statistical
modeling,
quantification
with
different
through
numerical
simulation.
This
article
examines
critical
causes
flooding,
outlines
principal
methodologies,
details
each
method’s
features,
compares
their
strengths,
limitations,
uncertainties.
paper
advocates
for
an
integrated
approach
encompassing
climate
change,
ocean–land
systems,
topography,
human
activity,
land
use,
hazard
chains
enhance
our
understanding
mechanisms.
includes
adopting
Earth
system
modeling
framework
holistic
coupling
components,
merging
process-based
data-driven
models,
enhancing
model
grid
resolution,
refining
dynamical
frameworks,
comparing
complex
physical
models
more
straightforward
methods,
exploring
advanced
data
assimilation,
machine
learning,
quasi-real-time
forecasting
researchers
emergency
responders.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(9), P. 3694 - 3694
Published: April 26, 2024
Against
the
backdrop
of
global
warming
and
rising
sea
levels
coupled
with
increasing
urbanization,
flood
risks
for
plain
cities
have
intensified.
This
study
takes
Liaocheng
City
as
its
research
object
constructs
a
regional
risk
assessment
model
based
on
combination
subjective
objective
multi-weight
methods.
The
sets
weights
according
to
different
return
periods
from
three
perspectives:
severity
disaster-causing
factors,
exposure
disaster-prone
environments,
vulnerability
disaster-bearing
bodies.
It
also
uses
subjective–objective
adopts
CRITIC-entropy
environments
bodies,
AHP
criterion
layer.
Based
GIS
spatial
analysis
technology,
examination
zoning
disasters
at
county
scale
were
carried
out.
results
show
that,
unlike
existing
weighting
methods
machine
learning
methods,
this
method
can
simultaneously
avoid
subjectivity
uncertainty
parameters,
thus
enabling
more
accurate
decision-making
be
obtained.
distribution
comprehensive
is
high
in
central
western
parts
relatively
low
south
north,
while
area
characterized
by
very
concentrated
Dongchangfu
District
Guanxian
County.
With
gradual
increase
periods,
overall
medium-to-very-high-risk
areas
gradually
shrinks,
very-high-risk
moves
but
maintains
stable
rule.
Flood
an
important
basic
process
disaster
prevention
mitigation
cities,
provide
reference
similar
cities.
