Journal of Water and Climate Change,
Journal Year:
2024,
Volume and Issue:
15(8), P. 3939 - 3965
Published: July 8, 2024
ABSTRACT
Global
climate
change
is
a
phenomenon
resulting
from
the
complex
interaction
of
human
influences
and
natural
factors.
These
changes
lead
to
imbalances
in
systems
as
activities
such
greenhouse-gas
emissions
increase
atmospheric
gas
concentrations.
This
situation
affects
frequency
intensity
events
worldwide,
with
floods
being
one
them.
Floods
manifest
water
inundations
due
factors
rainfall
patterns,
rising
temperatures,
erosion,
sea-level
rise.
cause
significant
damage
sensitive
areas
residential
areas,
agricultural
lands,
river
valleys,
coastal
regions,
adversely
impacting
people's
lives,
economies,
environments.
Therefore,
flood
risk
has
been
investigated
decision-making
processes
Diyarbakır
province
using
analytical
hierarchy
process
(AHP)
method
future
disaggregation
global
model
data.
HadGEM-ES,
GFDL-ESM2M,
MPI-ESM-MR
models
RCP4.5
RCP8.5
scenarios
were
used.
Model
data
disaggregated
equidistance
quantile
matching
method.
The
study
reveals
flood-risk
findings
HadGEM-ES
while
no
was
found
GFDL-ESM2M
models.
In
AHP
method,
analysis
conducted
based
on
historical
across
interpreted
alongside
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(17), P. 7489 - 7489
Published: Aug. 29, 2024
Floods,
caused
by
intense
rainfall
or
typhoons,
overwhelming
urban
drainage
systems,
pose
significant
threats
to
areas,
leading
substantial
economic
losses
and
endangering
human
lives.
This
study
proposes
a
methodology
for
flood
assessment
in
areas
using
multiclass
classification
approach
with
Deep
Neural
Network
(DNN)
optimized
through
hyperparameter
tuning
genetic
algorithms
(GAs)
leveraging
remote
sensing
data
of
dataset
the
Ibadan
metropolis,
Nigeria
Metro
Manila,
Philippines.
The
results
show
that
DNN
model
significantly
improves
risk
accuracy
(Ibadan-0.98)
compared
datasets
containing
only
location
precipitation
(Manila-0.38).
By
incorporating
soil
into
model,
as
well
reducing
number
classes,
it
is
able
predict
risks
more
accurately,
providing
insights
proactive
mitigation
strategies
planning.
Journal of Hydrology,
Journal Year:
2024,
Volume and Issue:
637, P. 131406 - 131406
Published: May 24, 2024
Urban
pluvial
flash
flooding
(PFF),
driven
by
extreme
weather
and
urban
expansion,
introduces
complex
challenges
that
arise
from
the
dynamic
interaction
of
rainfall
hazard,
road
vulnerability,
traffic
exposure.
These
three
critical
components
must
be
interconnected
to
provide
a
comprehensive
prediction
roadway
PFF
risk.
Our
integrated
approach
combines
historical
data
real-time
Waze
flood
alerts
using
simplified
physics-based
model
hybrid
machine
learning
methods
predict
risk
at
segment
scale.
In
Dallas
case
study
with
four
intersections,
we
trained
multiple
models
15
storms
tested
on
5
storms.
The
XGBoost
method
excels
in
test
precision,
while
Random
Forest
offers
better
recall,
both
outperform
Support
Vector
Machines
(SVM).
choice
between
depends
factors
such
as
negative
class
(prediction
unflooded
areas)
uncertainty
false
positive
cost
(i.e.,
predicting
no
incorrectly).
For
study,
our
could
boost
awareness,
enhance
safety,
improve
management
correctly
73%
observations
during
storm
events.
E3S Web of Conferences,
Journal Year:
2025,
Volume and Issue:
604, P. 01002 - 01002
Published: Jan. 1, 2025
Flood
disaster
mitigation
efforts
are
carried
out
through
traffic
engineering
management
to
reduce
the
negative
impacts
of
flooding
on
community
and
infrastructure,
where
these
require
a
comprehensive
plan
involve
cooperation
between
government,
authorities,
emergency
services
surrounding
community.
This
study
aims
determine
scenario
that
can
congestion
improve
performance
during
floods.
analyzes
road
network
in
Gedebage
District
Ujung
Berung
City
which
affected
by
floods
under
normal
conditions,
flood
conditions
(do-nothing),
when
is
applied
(do-something)
developing
an
origin-destination
matrix
formed
using
Furness
method,
modelling
PTV
Visum
analyzed
2023
Indonesian
Road
Capacity
Guidelines
method.
The
results
indicate
there
increase
with
implementation
do-something
2.
recommends
stakeholders
implement
providing
information
mapping
flood-prone
areas,
early
warning
systems,
evacuation
routes
routes,
alternative
routes.
