Development of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Dec. 28, 2024
A
precise
streamflow
forecast
is
crucial
in
hydrology
for
flood
alerts,
water
quantity
and
quality
management,
disaster
preparedness.
Machine
learning
(ML)
techniques
are
commonly
employed
hydrological
prediction;
however,
they
still
face
certain
drawbacks,
such
as
the
need
to
optimize
appropriate
predictors,
ability
of
models
generalize
across
different
time
horizons,
analysis
high-dimensional
series.
This
research
aims
address
these
specific
drawbacks
by
developing
a
novel
framework
forecasting.
Specifically,
hybrid
ML
model,
WKELM-R,
developed
predict
based
on
daily
discharge
precipitation.
The
model
combines
ridge
regression
(RR),
locally
weighted
linear
(LWLR),
kernel
extreme
machine
(KELM)
enhance
multi-step-ahead
predictions
accounting
both
nonlinear
characteristics.
In
data
preprocessing,
this
study
applies
multivariate
variational
mode
decomposition
(MVMD)
handle
non-stationarity
complexity,
Boruta-XGBoost
feature
selection
select
optimal
inputs
decrease
dimension,
gradient-based
optimizer
(GBO)
adjustment
parameters
overcome
predictors.
To
demonstrate
real-world
conditions
WKELM-R
was
applied
watershed
North
Dakota,
USA
three
horizons.
results
were
compared
with
those
from
existing
standalone
multi-criteria
decision-making
(MCDM),
demonstrating
efficacy
unique
capabilities
new
forecasting
(for
testing
level
at
t
+
3:
R
=
0.992,
RMSE
0.426,
NSE
0.983;
7:
0.997,
0.249,
0.994;
14:
0.996,
0.304,
0.991).
Language: Английский
Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data
Atmosphere,
Journal Year:
2024,
Volume and Issue:
15(12), P. 1469 - 1469
Published: Dec. 9, 2024
Severe
air
pollution
problems
continue
to
increase
because
of
accelerated
industrialization
and
urbanization.
Specifically,
fine
particulate
matter
(PM2.5)
causes
respiratory
cardiovascular
diseases,
according
the
World
Health
Organization
(WHO),
millions
premature
deaths
significant
health
burdens
annually.
Therefore,
PM2.5
concentration
forecasting
is
essential.
This
study
proposed
a
method
forecast
concentrations
one
hour
after
using
Sequence-to-Sequence
Attention
(Seq2Seq-attention).
The
selects
neighboring
stations
minimum
redundancy
maximum
relevance
(mRMR)
integrates
their
data
convolutional
neural
network
(CNN).
attention
score
Seq2Seq
are
used
on
integrated
hour.
performance
validated
through
two
case
studies.
first
comparison
evaluated
conventional
against
scores.
second
results
with
without
considering
stations.
showed
that
improved
index
(Root
Mean
Square
Error
(RMSE):
3.48%p,
Absolute
(MAE):
8.60%p,
R2:
0.49%p,
relative
Root
(rRMSE):
3.64%p,
Percent
Bias
(PBIAS):
59.29%p).
stations’
can
be
more
effective
in
than
standalone
station
(RMSE:
5.49%p,
MAE:
0.51%p,
0.67%p,
rRMSE:
5.44%p,
PBIAS:
46.56%p).
confirmed
effectively
Language: Английский