ISPRS International Journal of Geo-Information,
Год журнала:
2024,
Номер
13(9), С. 339 - 339
Опубликована: Сен. 22, 2024
This
study
investigates
the
spatial
disparities
in
flood
risk
and
social
vulnerability
across
66,543
census
tracts
Conterminous
United
States
(CONUS),
emphasizing
urban–rural
differences.
Utilizing
American
Community
Survey
(ACS)
2016–2020
data,
we
focused
on
16
factors
representing
socioeconomic
status,
household
composition,
racial
ethnic
minority
housing
transportation
access.
Principal
Component
Analysis
(PCA)
reduced
these
variables
into
five
principal
components:
Socioeconomic
Disadvantage,
Elderly
Disability,
Housing
Density
Vehicle
Access,
Youth
Mobile
Housing,
Group
Quarters
Unemployment.
An
additive
model
created
a
comprehensive
Social
Vulnerability
Index
(SVI).
Statistical
analysis,
including
Mann–Whitney
U
test,
indicated
significant
differences
between
urban
rural
areas.
Spatial
cluster
analysis
using
Local
Indicators
of
Association
(LISA)
revealed
high
clusters,
particularly
regions
along
Gulf
Coast,
Atlantic
Seaboard,
Mississippi
River.
Global
local
regression
models,
Ordinary
Least
Squares
(OLS)
Geographically
Weighted
Regression
(GWR),
highlighted
vulnerability’s
variability
localized
impacts
risk.
The
results
showed
substantial
regional
disparities,
with
areas
exhibiting
higher
risks
vulnerability,
especially
southeastern
centers.
also
that
Unemployment,
Access
are
closely
related
to
areas,
while
relationship
such
as
Disability
is
more
pronounced.
underscores
necessity
for
targeted,
region-specific
strategies
mitigate
enhance
resilience,
where
converge.
These
findings
provide
critical
insights
policymakers
planners
aiming
address
environmental
justice
promote
equitable
management
diverse
geographic
settings.
Geocarto International,
Год журнала:
2022,
Номер
37(26), С. 14495 - 14527
Опубликована: Июнь 11, 2022
This
study
proposes
a
new
groundwater
potentiality
model
(GPM)
in
the
Bisha
watershed,
Saudi
Arabia,
by
integrating
logistic-regression
(LR)-weighted
and
fuzzy
logic-based
ensemble
machine
learning
(EML)
models
for
present
future
scenarios.
We
applied
random
forest,
bagging,
subspace
predicting
GPMs.
also
used
general
circulation
model's
(GCM)
minimum
maximum
representative
concentration
pathway
(RCP)
2.6
8.5
scenarios
GWP
mapping.
Results
showed
that
bagging
hybrid
(Area
under
Curve:
0.986)
outperformed
other
models.
predicted
4058.57
km2
as
very
high,
4267.45
4185.23
moderate,
4342.
low,
4430.24
low
potential
zones.
The
best
combined
with
climatic
conditions
shows
high
zones
would
cover
2319–2590
3100–2795
km2.
current
research
will
aid
development
of
long-term
sustainable
management
plans
increasing
effectiveness
Sustainability,
Год журнала:
2023,
Номер
15(17), С. 12729 - 12729
Опубликована: Авг. 23, 2023
Flood
disasters,
a
natural
hazard
throughout
human
history,
have
caused
significant
damage
to
safety
and
infrastructure.
This
paper
presents
systematic
study
using
databases
from
Springer
Link,
Science
Direct,
JSTOR,
Web
of
Science.
The
employs
the
PRISMA
report
analysis
method
examine
11
flood
disaster
case
studies
between
2010
2022.
findings
reveal
that
demographic
characteristics,
socioeconomic
status,
access
healthcare
crucially
determine
social
vulnerability
adverse
events.
Notably,
risk
perception
coping
capacity
also
received
substantial
attention
in
studies.
Unfortunately,
many
indicators
fail
adequately
consider
influence
these
factors.
effects
factors
make
communities
vulnerable
vary
across
stages
countries.
emphasizes
importance
considering
specific
situations
locations
when
understanding
origins
consequences
vulnerability.
article
concludes
by
offering
recommendations
customize
quantitative
contexts,
covering
aspects
such
as
temporal
context,
measurability,
indicator
relationships.
Abstract
Flooding
is
the
most
frequent
type
of
natural
disaster,
inducing
devastating
damage
at
large
and
small
spatial
scales.
Flood
exposure
analysis
a
critical
part
flood
risk
assessment.
While
studies
analyze
elements
separately,
it
crucial
to
perform
multi-parameter
consider
different
types
zones
gain
comprehensive
understanding
impact
make
informed
mitigation
decisions.
This
research
analyzes
population,
properties,
road
networks
potentially
exposed
100,
200,
500-year
events
county
level
in
State
Iowa
using
geospatial
analytics.
We
also
propose
index
fuzzy
overlay
help
find
impacted
county.
During
flooding,
results
indicate
that
county-level
percentage
displaced
length
can
reach
up
46%,
41%,
40%,
respectively.
found
buildings
roads
are
laid
residential
areas.
Also,
25%
counties
designated
as
very
high-exposure
study
many
stakeholders
identify
vulnerable
areas
ensure
equitable
distribution
investments
resources
toward
projects.
Applied Sciences,
Год журнала:
2024,
Номер
14(18), С. 8425 - 8425
Опубликована: Сен. 19, 2024
There
has
been
an
increase
in
the
frequency
of
hazards
associated
with
meteorological
and
hydrological
phenomena.
One
them
is
flash
floods
occurring
episodically
areas
concentrated
runoff—valleys
without
permanent
drainage.
In
opinion
residents
local
authorities,
these
are
potentially
safe
areas—they
not
threatened
by
therefore
often
occupied
buildings.
The
importance
addressing
land
use
planning
essential
for
sustainable
development
disaster
risk
reduction.
objective
this
research
was
to
assess
level
hazard
evaluate
its
presence
activities.
This
manuscript
fills
a
gap,
as
date
flood
threats
have
analyzed
individual
buildings
located
catchments
dry
valleys
temperate
climates.
More
than
12,000
first-order
were
analyzed.
study
covered
upland
area
East
Poland,
which
characterized
high
population
density
dispersed
rural
settlement.
Within
10
municipalities,
on
potential
episodic
runoff
lines
identified.
Qualitative
assessment
applied
ascertain
susceptibility
floods.
Such
criteria
slopes,
size,
shape
catchment
area,
cover,
among
others,
used.
Between
20%
lines,
about
900
sub-catchments
highly
or
very
susceptible
way
reduce
negative
effects
phenomena
undertake
proper
based
knowledge
geohazards,
including
However,
analysis
available
documents
shows
that
type
completely
taken
into
account
spatial
management
processes.
ISPRS International Journal of Geo-Information,
Год журнала:
2024,
Номер
13(9), С. 339 - 339
Опубликована: Сен. 22, 2024
This
study
investigates
the
spatial
disparities
in
flood
risk
and
social
vulnerability
across
66,543
census
tracts
Conterminous
United
States
(CONUS),
emphasizing
urban–rural
differences.
Utilizing
American
Community
Survey
(ACS)
2016–2020
data,
we
focused
on
16
factors
representing
socioeconomic
status,
household
composition,
racial
ethnic
minority
housing
transportation
access.
Principal
Component
Analysis
(PCA)
reduced
these
variables
into
five
principal
components:
Socioeconomic
Disadvantage,
Elderly
Disability,
Housing
Density
Vehicle
Access,
Youth
Mobile
Housing,
Group
Quarters
Unemployment.
An
additive
model
created
a
comprehensive
Social
Vulnerability
Index
(SVI).
Statistical
analysis,
including
Mann–Whitney
U
test,
indicated
significant
differences
between
urban
rural
areas.
Spatial
cluster
analysis
using
Local
Indicators
of
Association
(LISA)
revealed
high
clusters,
particularly
regions
along
Gulf
Coast,
Atlantic
Seaboard,
Mississippi
River.
Global
local
regression
models,
Ordinary
Least
Squares
(OLS)
Geographically
Weighted
Regression
(GWR),
highlighted
vulnerability’s
variability
localized
impacts
risk.
The
results
showed
substantial
regional
disparities,
with
areas
exhibiting
higher
risks
vulnerability,
especially
southeastern
centers.
also
that
Unemployment,
Access
are
closely
related
to
areas,
while
relationship
such
as
Disability
is
more
pronounced.
underscores
necessity
for
targeted,
region-specific
strategies
mitigate
enhance
resilience,
where
converge.
These
findings
provide
critical
insights
policymakers
planners
aiming
address
environmental
justice
promote
equitable
management
diverse
geographic
settings.