Escape routes and safe points in natural hazards. A case study for soil
Engineering Geology,
Год журнала:
2024,
Номер
340, С. 107683 - 107683
Опубликована: Авг. 13, 2024
Язык: Английский
A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide
Landslides,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 14, 2024
Язык: Английский
Topographic stress proxy as a new causative factor in landslide susceptibility mapping
Gondwana Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Язык: Английский
Interpretability study of earthquake-induced landslide susceptibility combining dimensionality reduction and clustering
Xianghang Bu,
Songhai Fan,
Zongxi Zhang
и другие.
Frontiers in Earth Science,
Год журнала:
2025,
Номер
13
Опубликована: Апрель 25, 2025
An
earthquake
of
magnitude
Ms5.8
struck
Barkam
City,
Aba
Prefecture,
Sichuan
Province,
China,
on
the
morning
10
June
2022.
This
was
followed
by
two
additional
earthquakes
magnitudes
Ms6.0
and
Ms5.2.
The
triggered
significant
geological
hazards,
impacting
City
surrounding
areas.
Using
Random
Forest
(RF)
Extreme
Gradient
Boosting
(XGBoost)
machine
learning
models,
we
assessed
landslide
susceptibility
in
identified
key
influencing
factors.
study
applied
SHAP
method
to
evaluate
importance
various
factors,
used
UMAP
for
dimensionality
reduction,
employed
HDBSCAN
clustering
algorithm
classify
data,
thereby
enhancing
interpretability
models.
results
show
that
XGBoost
outperforms
RF
terms
accuracy,
precision,
recall,
F1
score,
KC,
MCC.
primary
factors
occurrence
are
topographic
features,
seismic
activity,
precipitation
intensity.
research
not
only
introduces
innovative
techniques
methods
analysis
but
also
provides
a
scientific
foundation
emergency
response
post-disaster
planning
related
risks
following
City.
Язык: Английский
Short Paper: AI-Driven Disaster Warning System: Integrating Predictive Data with LLM for Contextualized Guideline Generation
Md. Abrar Faiaz,
Nowshin Nawar
Опубликована: Дек. 19, 2024
Язык: Английский
Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion
Remote Sensing,
Год журнала:
2024,
Номер
16(17), С. 3119 - 3119
Опубликована: Авг. 23, 2024
Landslides
are
most
severe
in
the
mountainous
regions
of
southwestern
China.
While
landslide
identification
provides
a
foundation
for
disaster
prevention
operations,
methods
utilizing
multi-source
data
and
deep
learning
techniques
to
improve
efficiency
accuracy
complex
environments
still
focus
research
difficult
issue
research.
In
this
study,
we
address
above
problems
construct
model
based
on
shifted
window
(Swin)
transformer.
We
chose
Ya’an,
which
has
terrain
experiences
frequent
landslides,
as
study
area.
Our
model,
fuses
features
from
different
remote
sensing
sources
introduces
loss
function
that
better
learns
boundary
information
target,
is
compared
with
pyramid
scene
parsing
network
(PSPNet),
unified
perception
(UPerNet),
DeepLab_V3+
models
order
explore
potential
test
models’
resilience
an
open-source
database.
The
results
show
Ya’an
database,
benchmark
networks
(UPerNet,
PSPNet,
DeepLab_v3+),
Swin
Transformer-based
optimization
improves
overall
accuracies
by
1.7%,
2.1%,
1.5%,
respectively;
F1_score
improved
14.5%,
16.2%,
12.4%;
intersection
over
union
(IoU)
16.9%,
18.5%,
14.6%,
respectively.
performance
optimized
excellent.
Язык: Английский
Mountain Landslide Risk Assessment Based on High Resolution and High Quality Dem from Airborne Lidar: A Case Study in Jiuzhaigou, Sichuan, China
Опубликована: Янв. 1, 2024
Язык: Английский
Wildfire Risk Assessment to Overhead Transmission‐Line Based on Improved Analytic Hierarchy Process
Fire and Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 27, 2024
ABSTRACT
The
occurrence
of
wildfire
disasters
can
easily
trigger
tripping
in
overhead
transmission‐line,
thereby
posing
a
significant
threat
to
the
safe
and
stable
operation
power
system.
In
order
enhance
prevention
control
capability
risk
assessment
method
based
on
improved
analytic
hierarchy
process
(AHP)
is
proposed.
First,
main
factors
are
explored,
indicator
system
for
transmission‐line
constructed.
We
propose
novel
runaway
coefficient
fire
assessing
impact
sources
disaster.
Secondly,
mutual
information
used
avoid
subjective
arbitrariness
AHP
improve
reliability
each
index
weight.
results
show
that
about
82.14%
new
events
2023
Fujian
(China)
located
medium‐,
high‐,
very‐high‐risk
areas,
demonstrating
effectiveness
proposed
method.
This
methodology
offers
foundation
mitigate
wildfire.
Язык: Английский