Incorporating Dynamic Factors in Geological Hazard Risk Assessment: Integrating InSAR Deformation and Rainfall Conditions
Atmosphere,
Год журнала:
2025,
Номер
16(4), С. 360 - 360
Опубликована: Март 22, 2025
Geological
hazards,
particularly
in
mountainous
regions,
represent
significant
threats
to
life,
property,
and
the
environment.
In
this
study,
we
focus
on
Luoping
County,
Yunnan
Province,
China,
employing
SBAS-InSAR
technology
monitor
surface
deformation
between
8
October
2022
27
September
2024.
By
integrating
InSAR
data
with
10
static
disaster-causing
factors,
including
elevation,
slope,
aspect,
curvature,
distance
faults,
rivers,
roads,
engineering
geological
rock
groups,
geomorphological
types,
NDVI,
hazard
susceptibility
was
assessed
using
information
value
(IV)
model
value–random
forest
(IV-RF)
coupled
model.
Accuracy
validation
ROC
curves
indicated
that
IV-RF
model,
integrated
data,
achieved
highest
accuracy,
an
AUC
of
0.805.
Based
evaluation,
rainfall
intensity
introduced
as
a
triggering
factor
assess
risks
under
four
conditions:
10-year,
20-year,
50-year,
100-year
return
periods.
The
results
demonstrated
incorporating
significantly
improved
disaster
prediction
providing
more
reliable
sustainable
risk
assessment
outcomes.
This
study
underscores
critical
role
technology,
combined
conditions,
enhancing
precision
assessments,
offering
scientific
basis
for
prevention
mitigation
strategies
County
similar
regions.
Язык: Английский
Advancements in Artificial Intelligence Applications for Forest Fire Prediction
Forests,
Год журнала:
2025,
Номер
16(4), С. 704 - 704
Опубликована: Апрель 19, 2025
In
recent
years,
the
increasingly
significant
impacts
of
climate
change
and
human
activities
on
environment
have
led
to
more
frequent
occurrences
extreme
events
such
as
forest
fires.
The
recurrent
wildfires
pose
severe
threats
ecological
environments
life
safety.
Consequently,
fire
prediction
has
become
a
current
research
hotspot,
where
accurate
forecasting
technologies
are
crucial
for
reducing
economic
losses,
improving
management
efficiency,
ensuring
personnel
safety
property
security.
To
enhance
comprehensive
understanding
wildfire
research,
this
paper
systematically
reviews
studies
since
2015,
focusing
two
key
aspects:
datasets
with
related
tools
algorithms.
We
categorized
literature
into
three
categories:
statistical
analysis
physical
models,
traditional
machine
learning
methods,
deep
approaches.
Additionally,
review
summarizes
data
types
open-source
used
in
selected
literature.
further
outlines
challenges
future
directions,
including
exploring
risk
multimodal
learning,
investigating
self-supervised
model
interpretability
developing
explainable
integrating
physics-informed
models
constructing
digital
twin
technology
real-time
simulation
scenario
analysis.
This
study
aims
provide
valuable
support
natural
resource
enhanced
environmental
protection
through
application
remote
sensing
artificial
intelligence
Язык: Английский