PeerJ Computer Science,
Год журнала:
2024,
Номер
10, С. e2416 - e2416
Опубликована: Окт. 31, 2024
Specifically,
Iraq
is
threatened
in
its
second-largest
northern
city,
Mosul,
by
the
collapse
of
Mosul
Dam
due
to
problems
at
root
dam,
causing
a
wave
floods
that
will
cause
massive
loss
life,
resources,
and
public
property.
The
objective
this
study
effectively
monitor
level
dam
water
predicting
held
In
anticipation
achieving
flood
stage
breaking
supporting
behavior
through
formation
14-day
time
series
data
predict
seven
days
later.
Used
six
deep
learning
models
(deep
neural
network
(DNN),
convolutional
(CNN),
long
short-term
memory
(CNN-LSTM),
CNN-LSTM-Skip
CNN-LSTM
Skip
Attention)
were
trained
dam;
these
levels
being
under
surveillance
prepared
case
danger,
alert
people
potential
threats
depending
on
dam’s
level.
These
created
from
actual
sets
it’s
fundamental
historical
reading
for
13
years
(1993–2006)
stored
was
adopted
coordination
with
Iraqi
Ministry
Water
Resources
Centre
Research
Dams
University.
methodology
applied
shows
model’s
performance
efficiency
prediction
results’
low
error
rate.
It
also
compares
those
practical
results
determine
adopt
performance-efficient
model
lower
comparison
proved
accuracy
results,
superior
model,
it
has
highest
ability
perform
high
MAE
=
0.087153
steps
0
s
196
ms/step
0.00067.
current
demonstrated
Dam,
which
suffers
foundation
may
future.
Therefore,
must
be
monitored
accurately.
aims
test
effectiveness
proposed
after
evaluating
their
applying
process
within
scenario
obtain
predictive
values
14
days.
showed
hybrid
correctly
accurately
obtaining
integrated
framework
required
scenario.
concluded
possible
enhance
identify
risk
an
early
stage,
allows
proactive
crisis
management
sound
decision-making,
thus
mitigating
adverse
effects
crises
safety
infrastructure
reducing
human
losses
areas
along
Tigris
River.
Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102342 - 102342
Опубликована: Июнь 7, 2024
Precise
traffic
flow
prediction
is
a
central
component
of
advancing
intelligent
transportation
systems,
providing
essential
insights
for
optimizing
management,
reducing
travel
times,
and
alleviating
congestion.
This
study
introduces
an
efficient
deep
learning
approach
that
synergistically
integrates
the
benefits
wavelet-based
denoising
Recurrent
Neural
Networks
(RNNs).
integrated
methodology
introduced
to
effectively
capture
inherent
nonlinearity
temporal
dependencies
in
time
series
data.
Specifically,
Long
Short-Term
Memory
(LSTM)
Gated
Unit
(GRU)
are
address
challenges
associated
with
accurately
forecasting
flow.
To
enhance
quality,
data
preprocessed
using
exponential
smoothing
filtering
as
filters,
eliminating
outliers.
The
effectiveness
proposed
techniques
evaluated
measurements
collected
from
diverse
highway
locations
across
California,
including
Old
Bayshore
highway,
situated
south
Interstate
880
(I880),
Ashby
Ave
positioned
west
80
(I80)
San
Francisco
Bay
Area.
results
obtained
through
integrating
both
architectures,
LSTM
GRU,
within
wavelet
transform-based
filter
demonstrate
enhancement
performance.
Symlet
Haar
wavelets
achieved
high
performance
average
R2
0.982
0.9811,
respectively.
Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102504 - 102504
Опубликована: Июль 14, 2024
Accurate
wind
power
prediction
is
critical
for
efficient
grid
management
and
the
integration
of
renewable
energy
sources
into
grid.
This
study
presents
an
effective
deep-learning
approach
that
improves
short-term
forecasting
accuracy.
The
method
incorporates
a
Variational
Autoencoder
(VAE)
with
self-attention
mechanism
applied
in
both
encoder
decoder.
empowers
model
to
leverage
VAE's
strengths
time-series
modeling
nonlinear
approximation
while
focusing
on
most
relevant
features
within
data.
effectiveness
this
evaluated
through
comprehensive
comparison
eight
established
deep
learning
methods,
including
Recurrent
Neural
Networks
(RNNs),
Long
Short-Term
Memory
(LSTM)
networks,
Bidirectional
LSTMs
(BiLSTMs),
Convolutional
(ConvLSTMs),
Gated
Units
(GRUs),
Stacked
Autoencoders
(SAEs),
Restricted
Boltzmann
Machines
(RBMs),
vanilla
VAEs.
Real-world
data
from
five
turbines
France
Turkey
used
evaluation.
Five
statistical
metrics
are
employed
quantitatively
assess
performance
each
method.
results
indicate
SA-VAE
consistently
outperformed
other
models,
achieving
highest
average
R2
value
0.992,
demonstrating
its
superior
predictive
capability
compared
existing
techniques.
Journal of Asian and African Studies,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 28, 2025
This
study
attempts
to
examine
the
water
crisis
dynamics
in
Bangalore,
elucidating
complex
interplay
between
geopolitical
tensions,
climate
change
impacts,
and
anthropogenic
factors
that
intensify
regional
scarcity.
Through
a
rigorous
analysis
of
secondary
data,
this
research
highlights
critical
role
disputes
crisis.
It
also
emphasizes
exacerbating
effects
variability,
evidenced
by
erratic
monsoon
patterns
inadequate
groundwater
replenishment.
Furthermore,
explores
how
such
as
rapid
urbanization
unsustainable
extraction
compound
The
urgency
situation
prompts
proposal
strategic
adaptation
mitigation
measures.
