Water,
Journal Year:
2024,
Volume and Issue:
16(18), P. 2636 - 2636
Published: Sept. 17, 2024
This
paper
develops
a
method
integrating
Geographic
Information
Systems
(GIS)
and
the
Decision-Making
Trials
Evaluation
Laboratory
(DEMATEL)
for
analysis
of
factors
influencing
urban
flood
risk
identification
flood-prone
areas.
The
is
based
on
nine
selected
factors:
land
use/land
cover
(LULC:
ratio
built-up
areas,
greenery
areas),
elevation,
slope,
population
density,
distance
from
river,
soil,
Topographic
Wetness
Index
(TWI),
Normalized
Difference
Vegetation
(NDVI).
DEMATEL
used
to
determine
cause–effect
relationship
between
factors,
allowing
key
criteria
their
weights
be
determined.
LULC
density
were
identified
as
most
important
floods.
was
applied
case
study—the
Serafa
River
watershed
(Poland),
an
urbanized
catchment
covering
housing
estates
cities
Kraków
Wieliczka
frequently
affected
by
flooding.
GIS
publicly
available
data
using
QGIS
with
obtained
vulnerable
45%
total
area
classified
areas
very
high
or
level
risk.
results
match
actual
inundation
incidents
that
occurred
in
recent
years
this
area.
study
shows
potential
possibility
DEMATEL-GIS
significance
designate
Soil Advances,
Journal Year:
2024,
Volume and Issue:
2, P. 100017 - 100017
Published: Aug. 31, 2024
Understanding
the
impact
of
changes
in
land
use/land
cover
(LULC)
on
carbon
sequestration
(Cseq)
and
emission
leads
to
achieving
sustainable
development
goals
(SDGs).
For
this,
Business-As-Usual
(BAU)
Sustainable
Development
(SD)
scenarios
were
examined
Azarshar
city,
Iran
which
is
faced
with
urban
intensification.
The
spatiotemporal
dynamics
cycle
influences
various
growth
indicators
are
still
unclear
even
under
climate
change,
rapid
urbanization,
ecological
deterioration.
In
this
research,
total
storage
(Cts)
Cseq
determined
at
four
pools
i.e.,
aboveground
(AGC),
belowground
(BGC),
dead
organic
(DeOC),
soil
(SOC).
This
research
revealed
a
successful
implementation
integrated
CA-Markov
InVEST
models
delineating
LULC
between
2013
2033.
It
was
concluded
that
resources
management
play
crucial
role
decreasing
along
increasing
across
study
area.
modelling
results
showed
significant
shifting
from
barren
cropland
developed
uses.
goes
beyond
providing
supporting
evidence
expansion
key
factor
driving
aforementioned
changes,
but
also
illustrates
importance
remote
sensing
modelling,
especially
where
information
sparse.
Natural Hazards Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: July 1, 2024
Floods
have
claimed
lives
and
devastated
societal
ecological
systems.
Because
of
their
catastrophic
tendency
the
financial
fatalities
they
cause,
floods
become
more
significant
on
a
global
scale
in
recent
years.
In
Edo
State,
Nigeria,
flooding
is
frequent
threat
that
happens
annually
seriously
damages
both
property.
While
potential
cannot
entirely
be
eliminated,
geospatial-based
technologies
can
greatly
lessen
its
effects.
Nigeria's
flood-prone
study's
objectives
are
to
identify
inundated
places
provide
nuanced
mapping
flood
risk.
To
facilitate
determination
risk
index
(FRI),
fundamental
flood-predictive
features
were
determined
by
taking
into
consideration
elevation,
slope,
distance
from
river,
rainfall
intensity,
land
use/land
cover,
soil
texture,
topographic
roughness
index,
wetness
normalized
difference
vegetation
(NDVI),
runoff
coefficient,
aspect,
drainage
capacity,
flow
accumulation,
sediment
transport
stream
power
index.
The
significance
each
predictive
factor
analytic
hierarchy
procedure
(AHP)
was
gathering
expert
views
perspectives
public
entities.
A
map
created
processing
gathered
data
using
AHP
ArcGIS
10.5
framework.
multicollinearity
(MC)
estimation
applied
assess
model's
predictability.
results
FRI
showed
there
high
extremely
severe
zones
affected
roughly
26
9%
area,
respectively.
Flood
risks
been
identified
as
predominant
south
region
study
which
characterized
low
elevation
wetness,
It
resultant
vulnerability
maps
agreed
with
past
occurrences
previously
experienced
research
demonstrating
technique's
efficacy
locating
locations
plagued
flooding.
Linear
regression
(R2)
analysis
further
conducted
evaluate
scientific
reliability
utilized
methodology;
this
shows
0.816
(81.6%)
dependability.
Consequently,
long-lasting
implementation
predictions,
warning
systems,
mitigation
strategies
may
achieved.
Water,
Journal Year:
2024,
Volume and Issue:
16(18), P. 2636 - 2636
Published: Sept. 17, 2024
This
paper
develops
a
method
integrating
Geographic
Information
Systems
(GIS)
and
the
Decision-Making
Trials
Evaluation
Laboratory
(DEMATEL)
for
analysis
of
factors
influencing
urban
flood
risk
identification
flood-prone
areas.
The
is
based
on
nine
selected
factors:
land
use/land
cover
(LULC:
ratio
built-up
areas,
greenery
areas),
elevation,
slope,
population
density,
distance
from
river,
soil,
Topographic
Wetness
Index
(TWI),
Normalized
Difference
Vegetation
(NDVI).
DEMATEL
used
to
determine
cause–effect
relationship
between
factors,
allowing
key
criteria
their
weights
be
determined.
LULC
density
were
identified
as
most
important
floods.
was
applied
case
study—the
Serafa
River
watershed
(Poland),
an
urbanized
catchment
covering
housing
estates
cities
Kraków
Wieliczka
frequently
affected
by
flooding.
GIS
publicly
available
data
using
QGIS
with
obtained
vulnerable
45%
total
area
classified
areas
very
high
or
level
risk.
results
match
actual
inundation
incidents
that
occurred
in
recent
years
this
area.
study
shows
potential
possibility
DEMATEL-GIS
significance
designate
