Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 24, 2025
Abstract
Climate
change
and
increased
human
activity
have
resulted
in
an
increase
the
frequency
intensity
of
wildfires.
Effective
wildfire
risk
assessment
is
essential
for
disaster
prevention,
resource
protection,
regional
stability.
Existing
studies
often
overlook
spatial
heterogeneity
temporal
patterns
wildfires,
with
limited
county-scale
quantitative
assessments.
To
address
these
gaps,
multidimensional
framework
Sichuan
Province
was
proposed,
combining
characterization
modeling.
Temporal
trends
mutation
wildfires
from
2001
to
2023
were
analyzed
using
Mann-Kendall
test.
Additionally,
model
constructed
by
hazard
vulnerability
Specifically,
assessed
Multiscale
Geographically
Weighted
Regression
(MGWR)
capturing
driving
factors.
Vulnerability
through
Multi-Criteria
Decision
Analysis
(MCDA)
approach
identify
areas
high
their
factor
importance.
The
results
indicated
a
significant
rise
particularly
during
winter
non-fire
prevention
periods.
MGWR
effectively
captured
heterogeneity,
identifying
highest
levels
southwestern
Sichuan,
Liangshan
Prefecture
Panzhihua
City.
High
scattered,
mainly
across
southwestern,
southern,
northern
Sichuan.
integrated
revealed
that
its
surrounding
counties
exhibited
significantly
higher
than
other
regions,
while
eastern
northeastern
regions
demonstrated
lowest
risk.
This
study
provides
scientific
foundation
targeted
management,
emergency
response
strategies
Province,
offering
valuable
insights
policymakers
managers.
Язык: Английский
Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility
Remote Sensing,
Год журнала:
2025,
Номер
17(9), С. 1608 - 1608
Опубликована: Май 1, 2025
Landslide
susceptibility
mapping
is
a
crucial
tool
for
landslide
disaster
risk
management.
However,
the
spatial
heterogeneity
of
conditioning
factors
affects
accuracy
predictions.
This
study
proposes
novel
method
combining
GeoDetector
and
geographical
weighted
random
forest
(GeoD-GWRF),
local
machine
learning
approach.
The
GeoD-GWRF
model
can
select
from
perspective
differentiation
interpret
influence
on
landslides
at
scale.
model’s
applicability
verified
using
Luhe
County,
Guangdong
Province,
as
case
study.
Compared
to
traditional
model,
achieves
higher
prediction
(AUC
=
0.942).
In
addition,
applicable
broader
areas
provide
more
targeted
results.
offers
valuable
reference
exploring
in
mapping.
Язык: Английский