Advances in environmental engineering and green technologies book series,
Journal Year:
2023,
Volume and Issue:
unknown, P. 115 - 129
Published: Dec. 30, 2023
The
goal
of
the
chapter
on
earthquake
multi-magnificence
detection
and
use
synthetic
intelligence
is
to
discover
exhibit
usage
device
learning
AI
techniques
for
appropriately
efficiently
detecting
different
lessons
earthquakes.
seeks
offer
a
complete
understanding
strategies
spotlight
capability
in
advancing
this
subject.
present
detailed
analysis
existing
recommend
novel
AI-primarily
based
that
could
enhance
category
accuracy
timeliness.
Conventional
seismology
commonly
focus
earthquakes
an
unmarried
seismic
event.
However,
algorithms
can
investigate
significant
quantity
information,
which
includes
ancient
facts,
geological
capabilities,
actual-time
signals,
become
aware
patterns
classify
into
multiple
instructions.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(16), P. 2947 - 2947
Published: Aug. 12, 2024
This
paper
systematically
reviews
remote
sensing
technology
and
learning
algorithms
in
exploring
landslides.
The
work
is
categorized
into
four
key
components:
(1)
literature
search
characteristics,
(2)
geographical
distribution
research
publication
trends,
(3)
progress
of
algorithms,
(4)
application
techniques
models
for
landslide
susceptibility
mapping,
detections,
prediction,
inventory
deformation
monitoring,
assessment,
extraction
management.
selections
were
based
on
keyword
searches
using
title/abstract
keywords
from
Web
Science
Scopus.
A
total
186
articles
published
between
2011
2024
critically
reviewed
to
provide
answers
questions
related
the
recent
advances
use
technologies
combined
with
artificial
intelligence
(AI),
machine
(ML),
deep
(DL)
algorithms.
review
revealed
that
these
methods
have
high
efficiency
detection,
hazard
mapping.
few
current
issues
also
identified
discussed.
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(16), P. 4098 - 4098
Published: Aug. 21, 2023
Earthquake
Disaster
Assessment
(EDA)
plays
a
critical
role
in
earthquake
disaster
prevention,
evacuation,
and
rescue
efforts.
Deep
learning
(DL),
which
boasts
advantages
image
processing,
signal
recognition,
object
detection,
has
facilitated
scientific
research
EDA.
This
paper
analyses
204
articles
through
systematic
literature
review
to
investigate
the
status
quo,
development,
challenges
of
DL
for
The
first
examines
distribution
characteristics
trends
two
categories
EDA
assessment
objects,
including
earthquakes
secondary
disasters
as
buildings,
infrastructure,
areas
physical
objects.
Next,
this
study
application
distribution,
advantages,
disadvantages
three
types
data
(remote
sensing
data,
seismic
social
media
data)
mainly
involved
these
studies.
Furthermore,
identifies
six
commonly
used
models
EDA,
convolutional
neural
network
(CNN),
multi-layer
perceptron
(MLP),
recurrent
(RNN),
generative
adversarial
(GAN),
transfer
(TL),
hybrid
models.
also
systematically
details
at
different
times
(i.e.,
pre-earthquake
stage,
during-earthquake
post-earthquake
multi-stage).
We
find
that
most
extensive
field
involves
using
CNNs
classification
detect
assess
building
damage
resulting
from
earthquakes.
Finally,
discusses
related
training
models,
opportunities
new
sources,
multimodal
DL,
concepts.
provides
valuable
references
scholars
practitioners
fields.
Frontiers in Environmental Science,
Journal Year:
2023,
Volume and Issue:
11
Published: May 9, 2023
Predicting
land
susceptibility
to
wind
erosion
is
necessary
mitigate
the
negative
impacts
of
on
soil
fertility,
ecosystems,
and
human
health.
This
study
first
attempt
model
hazards
through
application
a
novel
approach,
graph
convolutional
networks
(GCNs),
as
deep
learning
models
with
Monte
Carlo
dropout.
approach
applied
Semnan
Province
in
arid
central
Iran,
an
area
vulnerable
dust
storms
climate
change.
We
mapped
15
potential
factors
controlling
erosion,
including
climatic
variables,
characteristics,
lithology,
vegetation
cover,
use,
digital
elevation
(DEM),
then
least
absolute
shrinkage
selection
operator
(LASSO)
regression
discriminate
most
important
factors.
constructed
predictive
by
randomly
selecting
70%
30%
pixels,
training
validation
datasets,
respectively,
focusing
locations
severe
inventory
map.
The
current
LASSO
identified
eight
out
features
(four
property
categories,
speed,
evaporation)
Province.
These
were
adopted
into
GCN
model,
which
estimated
that
15.5%,
19.8%,
33.2%,
31.4%
total
characterized
low,
moderate,
high,
very
high
respectively.
under
curve
(AUC)
SHapley
Additive
exPlanations
(SHAP)
game
theory
assess
performance
interpretability
output,
AUC
values
for
datasets
at
97.2%
97.25%,
indicating
excellent
prediction.
SHAP
ranged
between
−0.3
0.4,
while
analyses
revealed
coarse
clastic
component,
use
effective
output.
Our
results
suggest
this
suite
methods
highly
recommended
future
spatial
prediction
other
environments
around
globe.
Geocarto International,
Journal Year:
2022,
Volume and Issue:
37(27), P. 17826 - 17852
Published: Oct. 14, 2022
In
geomorphological
hazard
studies,
selecting
DEM
data
with
the
proper
spatial
resolution
is
necessary
for
optimal
analysis
of
prediction
performance.
Henceforth,
accurate
in
landslide
susceptibility
study
also
crucial
this
perspective.
This
determines
scale
effects
derived
hydro-topographic
factors
LS
mapping
Rangpo
river
basin,
Sikkim
Himalaya,
India.
Five
different
i.e.,
ALOS
(12.5
m),
and
AW3D30,
SRTM,
ASTER
Cartosat-1
each
30
m
were
used
study.
Three
neural
network
algorithms
applied
to
produce
LSM.
The
results
investigation
revealed
that,
among
three
employed
techniques,
deep
learning
algorithm
performed
best.
proposed
unique
approach
combination
can
be
useful
precise
LSMs
hilly
areas
around
globe,
will
helpful
sustainable
development.
