Ecological Informatics,
Год журнала:
2024,
Номер
81, С. 102597 - 102597
Опубликована: Апрель 9, 2024
This
study
represents
the
first
application
of
Sentinel-2
remote
sensing
imagery
and
model
fusion
techniques
to
assess
chlorophyll-a
(Chla)
concentration
turbidity
in
Nansi
Lake,
Shandong
Province,
China,
from
2016
2022.
First,
we
innovatively
employed
stacking
method
fuse
eight
fundamentally
different
Machine
Learning
(ML)
models,
each
utilising
20
17
feature
bands,
resulting
development
a
robust
algorithm
for
estimating
Chla
Lake.
The
results
demonstrate
that
Stacking
Model
has
achieved
outstanding
theoretical
generalisation
capability.
Second,
sensitivity
extreme
value
data
sample
was
quantified,
found
compared
with
gradient
boosting
(XGBoost),
optimal
performance
improved
by
12%,
some
extent,
it
solved
problem
high-value
underestimation
low-value
overestimation.
SHapley
Additive
exPlanations
(SHAP)
revealed
features
such
as
Three
Bands,
Enhanced
Three,
Rrs492/Rrs560,
Rrs705/Rrs665
play
crucial
role
concentration.
For
estimation,
Normalized
Difference
Turbidity
Index
(NDTI),
Rrs705+Rrs560,
Rrs865-Rrs740
made
significant
contributions.
Finally,
utilised
create
spatiotemporal
maps
Lake
We
analysed
causes
water
quality
changes
explored
driving
factors.
Compared
previous
studies,
this
paper
provides
new
idea
monitoring
lake
parameters
using
high
resolution
image
precision
technology,
these
can
provide
reference
similar
area
research
decision-making
support
environment-related
departments.
Atmosphere,
Год журнала:
2024,
Номер
15(6), С. 671 - 671
Опубликована: Май 31, 2024
With
the
ongoing
advancement
of
globalization
significantly
impacting
ecological
environment,
continuous
rise
in
Land
Surface
Temperature
(LST)
is
increasingly
jeopardizing
human
production
and
living
conditions.
This
study
aims
to
investigate
seasonal
variations
LST
its
driving
factors
using
mathematical
models.
Taking
Wuhan
Urban
Agglomeration
(WHUA)
as
a
case
study,
it
explores
characteristics
employs
Principal
Component
Analysis
(PCA)
categorize
factors.
Additionally,
compares
traditional
models
with
machine-learning
select
optimal
model
for
this
investigation.
The
main
conclusions
are
follows.
(1)
WHUA’s
exhibits
significant
differences
among
seasons
demonstrates
distinct
spatial-clustering
different
seasons.
(2)
Compared
geographic
spatial
models,
Extreme
Gradient
Boosting
(XGBoost)
shows
better
explanatory
power
investigating
effects
LST.
(3)
Human
Activity
(HA)
dominates
influence
throughout
year
positive
correlation
LST;
Physical
Geography
(PG)
negative
Climate
Weather
(CW)
show
similar
variation
PG,
peaking
transition;
Landscape
Pattern
(LP)
weak
LST,
winter
while
being
relatively
inconspicuous
summer
transition.
Finally,
through
comparative
analysis
multiple
constructs
framework
exploring
features
aiming
provide
references
guidance
development
WHUA
regions.
Geoscience Frontiers,
Год журнала:
2024,
Номер
15(4), С. 101800 - 101800
Опубликована: Фев. 2, 2024
Hydro-morphological
processes
(HMP,
any
natural
phenomenon
contained
within
the
spectrum
defined
between
debris
flows
and
flash
floods)
are
globally
occurring
hazards
which
pose
great
threats
to
our
society,
leading
fatalities
economical
losses.
For
this
reason,
understanding
dynamics
behind
HMPs
is
needed
aid
in
hazard
risk
assessment.
In
work,
we
take
advantage
of
an
explainable
deep
learning
model
extract
global
local
interpretations
HMP
occurrences
across
whole
Chinese
territory.
We
use
a
neural
network
architecture
interpret
results
through
spatial
pattern
SHAP
values.
doing
so,
can
understand
prediction
on
hierarchical
basis,
looking
at
how
predictor
set
controls
overall
susceptibility
as
well
same
level
single
mapping
unit.
Our
accurately
predicts
with
AUC
values
measured
ten-fold
cross-validation
ranging
0.83
0.86.
This
predictive
performance
attests
for
excellent
skill.
The
main
difference
respect
traditional
statistical
tools
that
latter
usually
lead
clear
interpretation
expense
high
performance,
otherwise
reached
via
machine/deep
solutions,
though
interpretation.
recent
development
AI
key
combine
both
strengths.
explore
combination
context
modeling.
Specifically,
demonstrate
extent
one
enter
new
data-driven
interpretation,
supporting
decision-making
process
disaster
mitigation
prevention
actions.
International Journal of Information Technology,
Год журнала:
2024,
Номер
16(3), С. 1279 - 1292
Опубликована: Янв. 2, 2024
Abstract
The
big
Artificial
General
Intelligence
models
inspire
hot
topics
currently.
black
box
problems
of
(AI)
still
exist
and
need
to
be
solved
urgently,
especially
in
the
medical
area.
Therefore,
transparent
reliable
AI
with
small
data
are
also
urgently
necessary.
To
build
a
trustable
model
data,
we
proposed
prior
knowledge-integrated
transformer
model.
We
first
acquired
knowledge
using
Shapley
Additive
exPlanations
from
various
pre-trained
machine
learning
models.
Then,
used
construct
compared
our
Feature
Tokenization
Transformer
other
classification
tested
on
three
open
datasets
one
non-open
public
dataset
Japan
confirm
feasibility
methodology.
Our
results
certified
that
perform
better
(1%)
than
general
Meanwhile,
methodology
identified
self-attention
factors
is
nearly
same,
which
needs
explored
future
work.
Moreover,
research
inspires
endeavors
exploring
