IOP Conference Series Earth and Environmental Science,
Год журнала:
2024,
Номер
1418(1), С. 012055 - 012055
Опубликована: Дек. 1, 2024
Abstract
The
goal
of
this
study
is
to
determine
the
classification
infiltration
for
Micro-Hydro
Power
Planning
using
Random
Forest
(RF)
machine
learning
algorithm.
Utilizing
Landsat
8
satellite
imagery,
data
provides
a
comprehensive
basis
analyzing
various
environmental
factors
relevant
infiltration.
RF
algorithm
models
and
classifies
rates,
ensuring
precise
reliable
predictions
essential
effective
micro-hydro
power
planning.
model
evaluation
results
demonstrate
excellent
performance,
with
an
Overall
Accuracy
0.97
Kappa
Coefficient
0.96,
indicating
strong
agreement
between
predicted
actual
classifications.
High
Sensitivity,
Specificity
(0.99
all
classes),
User
values
(all
above
0.95)
underscore
model’s
ability
correctly
identify
categories
maintain
consistency
in
positive
negative
predictions.
Feature
importance
analysis
highlights
that
certain
spectral
bands
significantly
enhance
predictive
capability,
Band
3
playing
crucial
role
(importance
score
100),
followed
by
Bands
7
6.
These
capture
specific
signatures
associated
different
improving
performance
reliability.
research
contributes
Sustainable
Development
Goals
(SDGs),
supporting
SDG
6
(clean
water
sanitation),
(affordable
clean
energy),
9
(industry,
innovation,
infrastructure),
13
(climate
action),
15
(life
on
land)
through
improved
resource
management
stewardship.
Remote Sensing,
Год журнала:
2024,
Номер
16(15), С. 2715 - 2715
Опубликована: Июль 24, 2024
Le’an
Town,
located
in
the
southwest
of
Qingchuan
County,
Guangyuan
City,
Sichuan
Province,
boasts
a
unique
geographical
position.
The
town’s
terrain
is
complex,
and
its
geological
environment
fragile.
Multiple
phases
tectonic
movements
have
resulted
numerous
cracks
faults,
making
area
prone
to
landslides,
debris
flows,
other
disasters.
Additionally,
heavy
rainfall
fluctuating
groundwater
levels
further
exacerbate
instability
mountains.
Human
activities,
such
as
overdevelopment
deforestation,
significantly
increased
risk
Currently,
methods
for
landslide
prediction
Town
are
limited;
traditional
techniques
cannot
provide
precise
forecasts,
study
largely
covered
by
tall
vegetation.
Therefore,
this
paper
proposes
method
that
combines
SBAS-InSAR
technology
with
dynamic
changes
land
use
hydrological
conditions.
used
obtain
surface
deformation
information,
while
land-use
condition
data
incorporated
analyze
characteristics
potential
influencing
factors
areas.
innovation
lies
high-precision
monitoring
capability
integration
multi-source
data,
which
can
more
comprehensively
reveal
environmental
area,
thereby
achieving
accurate
predictions
development.
results
indicate
annual
subsidence
rate
most
areas
ranges
from
−10
0
mm,
indicating
slow
subsidence.
In
some
areas,
exceeds
−50
mm
per
year,
showing
significant
slope
aspect
differences,
reflecting
combined
effects
structures,
climatic
conditions,
human
activities.
It
evident
conditions
impact
on
occurrence
development
landslides.
utilizing
cross-verifying
it
techniques,
consistency
identified
be
enhanced,
improving
results.
This
provides
scientific
basis
early
warning
disasters
has
important
practical
application
value.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 19, 2024
With
the
global
land
use/land
cover
(LULC)
and
climate
change,
ecological
resilience
(ER)
in
typical
Karst
areas
has
become
focus
of
attention.
Its
future
development
trend
its
spatial
response
to
natural
anthropogenic
factors
are
crucial
for
understanding
changes
ecologically
fragile
human
behavior.
However,
there
is
still
a
lack
relevant
quantitative
research.
The
study
systematically
analyzed
characteristics
LULC
Southwest
China
with
over
past
20
years.
Drawing
on
landscape
ecology
research
paradigm,
potential-elasticity-stability
ER
assessment
model
was
constructed.
Revealing
heterogeneity
distribution,
annual
evolution,
under
different
scenarios
shared
socioeconomic
pathways
representative
concentration
(SSP-RCP)
future.
In
addition,
econometric
utilized
reveal
effect
mechanism
ER,
adaptive
strategies
were
proposed
promote
sustainable
China.
found
that
:
(1)
years,
showed
an
accelerated
change
trend,
decreased
declined
general,
significant
heterogeneity,
showing
distribution
pattern
"west
larger
than
east,
south
north,
reduction
west
slower
east."
(2)
Under
same
SSP
scenario,
increase
RCP
emission
concentration,
area
lowest-resilience
increased
significantly,
highest-resilience
decreased.
(3)
woodland
largest
contributor
per
unit
China,
grassland
main
type,
which
had
prominent
impact
area.
(4)
average
precipitation
normalized
difference
vegetation
index
(NDVI)
drivers
area,
economic
growth,
innovation,
optimization
industrial
structure
contributed
Overall,
integration
multi-scenario-based
modeling
not
only
provides
new
perspectives
mechanisms,
but
also
valuable
references
other
regions
around
world
achieve
development.
