Journal of Water and Climate Change,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 18, 2024
ABSTRACT
India's
diverse
geography
poses
significant
flood
risks,
addressed
in
this
study
through
the
geographic
information
system
and
multi-criteria
decision
analysis.
This
comprehensive
risk
assessment
considers
seven
parameters:
mean
annual
precipitation,
elevation,
slope,
drainage
density
(DD),
land
use
cover,
proximity
to
roads,
distance
rivers.
The
findings
indicate
that
vulnerability
is
primarily
influenced
by
rainfall,
with
DD,
use,
roads
rivers
also
playing
crucial
roles.
Experts
weighed
these
factors
create
a
thorough
map
using
normalized
rank
index
weight
index,
categorizing
areas
into
five
levels:
very
high,
moderate,
low,
low.
reveals
3.40%
of
area
at
high
risk,
32.65%
39.72%
moderate
20.97%
low
3.25%
risk.
These
results
highlight
how
human
natural
interact
influence
vulnerable
characterized
elevations,
steep
slopes,
densities,
or
roads.
provide
valuable
insights
for
policymakers,
scientists,
local
authorities
develop
strategies
mitigate
losses
across
varied
landscapes.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
23, P. 102682 - 102682
Published: Aug. 8, 2024
To
effectively
tackle
the
challenges
posed
by
climate
change,
it
is
crucial
to
enhance
accuracy
of
rainfall-runoff
models
ensure
reliability
amidst
changing
climatic
conditions.
Neural
networks,
renowned
for
their
ability
capture
complex
patterns
and
relationships
within
uncertain
input
output
data,
offer
valuable
tools
in
this
pursuit.
This
study
aims
evaluate
efficacy
two
neural
network
(NN)
models:
Radial
Basis
Function
Network
(RBFNN)
Model
Tree
M5
(MTM5NN).
These
are
assessed
both
individually
combination
with
Wavelet
(WT)
data
processing
technique
modeling
Kolar
River
watershed
located
Madhya
Pradesh,
India.
Fifteen
were
developed
employing
four
algorithms:
RBFNN
models,
WRBFNN
(RBF
model
integrating
wavelet
components
rainfall
as
inputs),
MTM5NN,
WMTM5NN
(MT
incorporating
inputs).
Initially,
runoff
underwent
normalization
applied
MTM5NN
networks.
Subsequently,
time
series
decomposed
using
transforms,
resulting
various
sub-time
signals
such
approximations
decompositions.
derived
then
utilized
specifically
designated
WMTM5NN.
The
most
effective
identified
was
8
WMTM5NN,
which
demonstrated
R2
values
close
0.97,
outperforming
other
models.
results
underscore
superior
performance
model,
highlighting
its
effectiveness
achieving
heightened
predicting
specific
watershed.
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
1(1)
Published: Dec. 2, 2024
Understanding
and
anticipating
the
impacts
of
climate
change
on
hydrological
processes
is
crucial
for
sustainable
water
resource
management.
This
study
investigates
projected
alterations
in
streamflow
within
Tamor
River
Basin,
Nepal,
under
changing
climatic
conditions,
utilizing
soil
assessment
tool
(SWAT).
Future
variables,
including
precipitation,
maximum,
minimum
temperature,
were
assessed
near
(2022–2047),
mid
(2048–2073),
far
future
(2074–2100)
periods
two
shared
socioeconomic
pathways
(SSPs):
SSP245
SSP585.
Bias-corrected
outputs
from
coupled
model
intercomparison
project,
phase
6
(CMIP6)
models
integrated
into
SWAT
to
simulate
basin's
response.
Results
indicate
that,
scenario,
annual
average
maximum
temperatures
are
expected
rise
by
~
0.046
°C
0.050
°C,
respectively,
with
a
12.70%
increase
precipitation.
Similarly,
SSP585
scenario
predicts
temperature
increases
0.063
0.085
alongside
an
11.90%
These
changes
result
significant
streamflow,
estimated
up
20%
end
twenty-first
century.
The
findings
this
research
provide
valuable
insights
policymakers
stakeholders,
facilitating
informed
decision-making
management
resources
face
change.
