Discover Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
4(1)
Published: Nov. 3, 2024
The
atmosphere's
fine
articulate
Matter
(PM2.5)
poses
various
health-related
risks.
Even
though
multiple
efforts
have
been
made
to
lower
the
emissions
of
these
substances,
mortality
rate
is
continuously
increasing,
requiring
immediate
inclination
scientific
community
towards
design
and
development
advanced
predictive
models.
Conventional
statistical
approaches
become
dormant
due
their
limitations
in
capturing
innate
relationships
between
pollutants,
particularly
for
predicting
PM2.5
concentrations.
In
contrast,
machine
deep
learning
techniques
shown
great
potential
forecasting
air
quality,
providing
more
accuracy
than
its
predecessor
techniques.
present
study
investigates
utilization
hybrid
by
integrating
models
with
improve
prediction
capabilities
concentration.
It
uses
datasets
from
World
Air
Quality
Index
(WAQI)
State
Global
(SOGA)
analyze
performance
on
both
daily
annual
data,
respectively.
This
ensures
model's
effectiveness
a
diversified
dataset.
implements
Random
Forest
(RF),
Polynomial
Regression
(PR),
XGBoost,
Extra
Tree
Regressor
(ETR)
coupled
Fully
Connected
Neural
Network
(FCNN),
Long
Short-Term
Memory
(LSTM),
Bi-directional
LSTM
(Bi-LSTM)
obtaining
optimized
results.
Finally,
after
thorough
investigation,
PR
model
FCNN
(PR-FCNN)
found
be
best
improved
R-squared
(R2)
values,
portraying
concentration
accurately.
Based
experimentation,
preset
recommends
implementing
approaches,
offering
better
especially
PM2.5.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(3), P. 467 - 467
Published: Jan. 25, 2024
Long-term
exposure
to
high
concentrations
of
fine
particles
can
cause
irreversible
damage
people’s
health.
Therefore,
it
is
extreme
significance
conduct
large-scale
continuous
spatial
particulate
matter
(PM2.5)
concentration
prediction
for
air
pollution
prevention
and
control
in
China.
The
distribution
PM2.5
ground
monitoring
stations
China
uneven
with
a
larger
number
southeastern
China,
while
the
sites
also
insufficient
quality
control.
Remote
sensing
technology
obtain
information
quickly
macroscopically.
possible
predict
based
on
multi-source
remote
data.
Our
study
took
as
research
area,
using
Pearson
correlation
coefficient
GeoDetector
select
auxiliary
variables.
In
addition,
long
short-term
memory
neural
network
random
forest
regression
model
were
established
estimation.
We
finally
selected
(R2
=
0.93,
RMSE
4.59
μg
m−3)
our
by
evaluation
index.
across
2021
was
estimated,
then
influence
factors
high-value
regions
explored.
It
clear
that
not
only
related
local
geographical
meteorological
conditions,
but
closely
economic
social
development.
Toxics,
Journal Year:
2025,
Volume and Issue:
13(4), P. 254 - 254
Published: March 28, 2025
Surface
air
pollution
affects
ecosystems
and
people’s
health.
However,
traditional
models
have
low
prediction
accuracy.
Therefore,
a
hybrid
model
for
accurately
predicting
daily
surface
PM2.5
concentrations
was
integrated
with
wavelet
(W),
convolutional
neural
network
(CNN),
bidirectional
long
short-term
memory
(BiLSTM),
gated
recurrent
unit
(BiGRU).
The
data
meteorological
factors
pollutants
in
Guangzhou
City
from
2014
to
2020
were
utilized
as
inputs
the
models.
W-CNN-BiGRU-BiLSTM
demonstrated
strong
performance
during
phase,
achieving
an
R
(correlation
coefficient)
of
0.9952,
root
mean
square
error
(RMSE)
1.4935
μg/m3,
absolute
(MAE)
1.2091
percentage
(MAPE)
7.3782%.
Correspondingly,
accurate
is
beneficial
control
urban
planning.
