Geosciences,
Год журнала:
2024,
Номер
15(1), С. 2 - 2
Опубликована: Дек. 26, 2024
This
study
aims
to
establish
a
scientific
and
methodological
basis
for
predicting
shoreline
positions
using
modern
data
analysis
machine
learning
techniques.
The
focus
area
is
5
km
section
of
the
Ural
coast
along
Baydaratskaya
Bay
in
Kara
Sea.
region
was
selected
due
its
diverse
geomorphological
features,
varied
lithological
composition,
significant
presence
permafrost
processes,
all
contributing
complex
patterns
change.
Applying
advanced
methods,
including
correlation
factor
analysis,
enables
identification
natural
signs
that
highlight
areas
active
coastal
retreat.
These
insights
are
valuable
arctic
development
planning,
as
they
help
recognize
zones
at
highest
risk
transformation.
erosion
process
can
be
conceptualized
comprising
two
primary
components
construct
predictive
model
first
random
variable
encapsulates
effects
local
structural
changes
coastline
alongside
fluctuations
climatic
conditions.
component
statistically
characterized
define
confidence
interval
variability.
second
represents
systematic
shift,
which
reflects
regular
average
over
time.
more
suited
modeling.
Thus,
information
processing
methods
allow
us
move
from
descriptive
numerical
assessments
dynamics
processes.
goal
ultimately
support
responsible
sustainable
highly
sensitive
region.
Data,
Год журнала:
2024,
Номер
9(12), С. 145 - 145
Опубликована: Дек. 9, 2024
This
study
aimed
to
develop
a
methodological
framework
for
predicting
shoreline
dynamics
using
machine
learning
techniques,
focusing
on
analyzing
generalized
data
without
distinguishing
areas
with
higher
or
lower
retreat
rates.
Three
sites
along
the
southwestern
Kara
Sea
coast
were
selected
this
investigation.
The
analyzed
key
coastal
features,
including
lithology,
permafrost,
and
geomorphology,
combination
of
field
studies
remote
sensing
data.
Essential
datasets
compiled
formatted
computer-based
analysis.
These
included
information
permafrost
geomorphological
characteristics
zone,
climatic
factors
influencing
shoreline,
measurements
bluff
top
positions
rates
over
defined
time
periods.
tops
determined
through
imagery
varying
resolutions
measurements.
A
novel
aspect
involved
employing
geostatistical
methods
analyze
erosion
rates,
providing
new
insights
into
dynamics.
analysis
allowed
us
identify
experiencing
most
significant
changes.
By
continually
refining
neural
network
models
these
datasets,
we
can
improve
our
understanding
complex
interactions
between
natural
evolution,
ultimately
aiding
in
developing
effective
management
strategies.
IEEE Geoscience and Remote Sensing Letters,
Год журнала:
2023,
Номер
21, С. 1 - 5
Опубликована: Ноя. 21, 2023
This
study
proposes
a
novel
method
to
detect
short-term
erosion
and
deposition
within
the
intertidal
zones
based
solely
on
waterlines
extracted
from
high-resolution
satellite
images.
The
judgment
of
or
is
intersection
waterlines,
which
can
reflect
topographic
changes.
procedures
have
been
demonstrated
by
its
application
large
Radial
Sand
Ridges
in
China,
where
are
drastic
due
strong
tidal
currents.
were
divided
into
four
elevation
zones,
Sentinel-2
images
used
obtain
spatial
distribution
at
each
sub-zone.
results
show
that
detected
greatly
linked
with
creeks.
had
further
for
preliminary
risk
assessment
wind
power
structures
area.
It
found
southern
more
possible
risk.
an
inexpensive
way
preliminarily
monitor
zones.
Geosciences,
Год журнала:
2024,
Номер
15(1), С. 2 - 2
Опубликована: Дек. 26, 2024
This
study
aims
to
establish
a
scientific
and
methodological
basis
for
predicting
shoreline
positions
using
modern
data
analysis
machine
learning
techniques.
The
focus
area
is
5
km
section
of
the
Ural
coast
along
Baydaratskaya
Bay
in
Kara
Sea.
region
was
selected
due
its
diverse
geomorphological
features,
varied
lithological
composition,
significant
presence
permafrost
processes,
all
contributing
complex
patterns
change.
Applying
advanced
methods,
including
correlation
factor
analysis,
enables
identification
natural
signs
that
highlight
areas
active
coastal
retreat.
These
insights
are
valuable
arctic
development
planning,
as
they
help
recognize
zones
at
highest
risk
transformation.
erosion
process
can
be
conceptualized
comprising
two
primary
components
construct
predictive
model
first
random
variable
encapsulates
effects
local
structural
changes
coastline
alongside
fluctuations
climatic
conditions.
component
statistically
characterized
define
confidence
interval
variability.
second
represents
systematic
shift,
which
reflects
regular
average
over
time.
more
suited
modeling.
Thus,
information
processing
methods
allow
us
move
from
descriptive
numerical
assessments
dynamics
processes.
goal
ultimately
support
responsible
sustainable
highly
sensitive
region.