Water,
Год журнала:
2024,
Номер
16(9), С. 1217 - 1217
Опубликована: Апрель 24, 2024
Based
on
the
data
of
Sichuan
Province
from
2007
to
2021,
extreme
climate
events
in
was
identified
by
statistical
method,
and
coupling
coordination
degree
water
resources-climate
system
separate
resource
analyzed.
difference
under
these
two
systems,
influence
mechanism
factors
is
The
results
show
that
types
gradually
transition
drought
precipitation
low
temperature
high
temperature.
When
are
not
considered,
generally
improved
distribution
more
concentrated.
Moreover,
a
simple
linear
relationship.
Learned Publishing,
Год журнала:
2024,
Номер
38(1)
Опубликована: Дек. 24, 2024
Abstract
The
aim
of
this
paper
is
to
highlight
the
situation
whereby
content
generated
by
large
language
model
ChatGPT
appearing
in
peer‐reviewed
papers
journals
recognized
publishers.
demonstrates
how
identify
sections
that
indicate
a
text
fragment
was
generated,
is,
entirely
created,
ChatGPT.
To
prepare
an
illustrative
compilation
appear
indexed
Web
Science
and
Scopus
databases
possessing
Impact
Factor
CiteScore
indicators,
SPAR4SLR
method
used,
which
mainly
applied
systematic
literature
reviews.
Three
main
findings
are
presented:
highly
regarded
premier
journals,
articles
bear
hallmarks
AI
models,
whose
use
not
declared
authors
(1);
many
these
identified
already
receiving
citations
from
other
scientific
works,
also
placed
found
(2);
and,
most
belong
disciplines
medicine
computer
science,
but
there
such
as
environmental
engineering,
sociology,
education,
economics
management
(3).
This
aims
continue
add
recently
initiated
discussion
on
models
like
creation
scholarly
works.
Advances in Meteorology,
Год журнала:
2024,
Номер
2024, С. 1 - 19
Опубликована: Апрель 22, 2024
The
rapid
development
of
urbanization
makes
the
phenomenon
urban
heat
islands
even
more
serious.
Predicting
impact
land
cover
change
on
island
has
become
one
research
hotspots.
Taking
Wuhan,
China,
as
an
example,
this
study
simulated
type
in
2020
through
Cellular
Automata-Markov-Chain
(CA-Markov)
model.
was
and
analyzed
conjunction
with
Weather
Research
&
Forecasting
Model
(WRF),
simulation
results
wind
velocity
temperature
were
confirmed
using
weather
station
observation
data.
Based
this,
Wuhan
2030
predicted.
found
to
be
well-fit
by
CA-Markov
use
data,
average
inaccuracy
about
2.5°C
for
stations.
Wind
speed
had
a
poor
fitting
effect;
error
roughly
2
m/s.
built-up
area
center
high
both
before
after
prediction,
water
low
area,
peak
happened
at
night.
According
forecast
results,
there
will
2030,
greater
intensity
than
2020.
