IEEE Access,
Год журнала:
2023,
Номер
11, С. 144631 - 144648
Опубликована: Янв. 1, 2023
Climate
change
is
a
phenomenon
that
sometimes
denied
or
trivialized.
However,
in
recent
years,
we
have
faced
extreme
phenomena
such
as
fires,
floods,
excessive
temperatures,
etc.
which
affect
our
physical
and
mental
condition
the
environment,
often
leading
to
significant
material
damage.
To
understand
these
problems
highlight
meteorological
phenomenological
changes
encountered
last
decade,
time
series
were
web-scraped
analyzed
from
several
open
data
sources:
weather
news
broadcast
Romania,
air
quality,
temperature,
The
extraction
organization
of
recorded
between
2009
2023
are
formulated
framework
can
be
reproduced
replicated
continue
monitoring.
exploratory
analysis
categorical
numerical
highlights
intricate
patterns
correlations
within
conditions
across
regions
seasons.
From
temperature
trends
quality
fluctuations,
study
underscores
dynamic
interplay
phenomena,
paving
way
for
informed
forecasting
deeper
climate
research.
At
same
time,
processing
includes
Latent
Dirichlet
Allocation,
K-prototype
clustering
analysis,
addition
K-means
with
dimensional
reduction
techniques,
all
employed
further
reveal
higher
occurrence.
Therefore,
this
paper,
propose
multiple
datasets
analytics,
extracting
valuable
information
on
identifying
exposed
phenomena.
Foods,
Год журнала:
2024,
Номер
13(21), С. 3385 - 3385
Опубликована: Окт. 24, 2024
Excessive
non-grain
production
of
farmland
(NGPF)
seriously
affects
food
security
and
hinders
progress
toward
Sustainable
Development
Goal
2
(Zero
Hunger).
Understanding
the
spatial
distribution
influencing
factors
NGPF
is
essential
for
agricultural
management.
However,
previous
studies
on
identification
have
mainly
relied
high-cost
methods
(e.g.,
visual
interpretation).
Furthermore,
common
machine
learning
techniques
difficulty
in
accurately
identifying
based
solely
spectral
information,
as
not
merely
a
natural
phenomenon.
Accurately
at
grid
scale
elucidating
its
emerged
critical
scientific
challenges
current
literature.
Therefore,
aims
this
study
are
to
develop
grid-scale
method
that
integrates
multisource
remote
sensing
data
enhance
precision
provide
more
comprehensive
understanding
factors.
To
overcome
these
challenges,
we
combined
images,
natural/anthropogenic
factors,
maximum
entropy
model
reveal
scale.
This
combination
can
detailed
information
quantify
integrated
influences
multiple
from
microscale
perspective.
In
case
Foshan,
China,
area
under
receiver
operating
characteristic
curve
0.786,
with
results
differing
by
only
1.74%
statistical
yearbook
results,
demonstrating
reliability
method.
Additionally,
total
error
our
result
lower
than
using
information.
Our
enhances
resolution
effectively
detects
small
fragmented
farmlands.
We
identified
elevation,
farming
radius,
population
density
dominant
affecting
NGPF.
These
offer
targeted
strategies
mitigate
excessive
The
advantage
lies
independence
negative
samples.
feature
applicability
other
cases,
particularly
regions
lacking
high-resolution
grain
crop-related
data.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 144631 - 144648
Опубликована: Янв. 1, 2023
Climate
change
is
a
phenomenon
that
sometimes
denied
or
trivialized.
However,
in
recent
years,
we
have
faced
extreme
phenomena
such
as
fires,
floods,
excessive
temperatures,
etc.
which
affect
our
physical
and
mental
condition
the
environment,
often
leading
to
significant
material
damage.
To
understand
these
problems
highlight
meteorological
phenomenological
changes
encountered
last
decade,
time
series
were
web-scraped
analyzed
from
several
open
data
sources:
weather
news
broadcast
Romania,
air
quality,
temperature,
The
extraction
organization
of
recorded
between
2009
2023
are
formulated
framework
can
be
reproduced
replicated
continue
monitoring.
exploratory
analysis
categorical
numerical
highlights
intricate
patterns
correlations
within
conditions
across
regions
seasons.
From
temperature
trends
quality
fluctuations,
study
underscores
dynamic
interplay
phenomena,
paving
way
for
informed
forecasting
deeper
climate
research.
At
same
time,
processing
includes
Latent
Dirichlet
Allocation,
K-prototype
clustering
analysis,
addition
K-means
with
dimensional
reduction
techniques,
all
employed
further
reveal
higher
occurrence.
Therefore,
this
paper,
propose
multiple
datasets
analytics,
extracting
valuable
information
on
identifying
exposed
phenomena.