Recent
publications
on
vulnerability
assessment
of
weather-related
disasters
exhibit
three
main
drawbacks:
(1)
minimal
explanation
high
contributing
features;
(2)
limitted
validating
conduction;
and
(3)
partial
presentation
validation
results.
To
bridge
this
research
gap,
our
offers
a
detailed
exploration
the
most
influential
factors
Socio-Economic
Vulnerability
Index
(SEVI),
developed
from
comprehensive
dataset
socio-economic
data.
The
SEVI
is
then
internally
conducted
through
Monte
Carlo
method,
providing
an
in-depth
evaluation
uncertainties
in
both
values
rankings,
along
with
sensitivity
analysis
features.
findings
reveal
that:
sub-districts
located
around
Han
River
tend
to
due
features
demo-graphic
structure;
Approximately
39%
26%
highly
vulnerable
low
bias
their
retain
unchanged
respectively,
thereby
ensuring
reliability
flood
risk
mitigation
strategy
implementation;
be
overes-timated,
vice
versa;
(4)
feature
causing
higher
variability
score
number
family
only
mother
children,
exceeding
5%;
(5)
showed
difference
one
based
extensive
expert
survey,
revealing
its
Water,
Год журнала:
2025,
Номер
17(5), С. 676 - 676
Опубликована: Фев. 26, 2025
Harmful
algal
blooms
(HABs)
have
emerged
as
a
significant
environmental
challenge,
impacting
aquatic
ecosystems,
drinking
water
supply
systems,
and
human
health
due
to
the
combined
effects
of
activities
climate
change.
This
study
investigates
performance
deep
learning
models,
particularly
Transformer
model,
there
are
limited
studies
exploring
its
effectiveness
in
HAB
prediction.
The
chlorophyll-a
(Chl-a)
concentration,
commonly
used
indicator
phytoplankton
biomass
proxy
for
occurrences,
is
target
variable.
We
consider
multiple
influencing
parameters—including
physical,
chemical,
biological
quality
monitoring
data
from
stations
located
west
Lake
Erie—and
employ
SHapley
Additive
exPlanations
(SHAP)
values
an
explainable
artificial
intelligence
(XAI)
tool
identify
key
input
features
affecting
HABs.
Our
findings
highlight
superiority
especially
Transformer,
capturing
complex
dynamics
parameters
providing
actionable
insights
ecological
management.
SHAP
analysis
identifies
Particulate
Organic
Carbon,
Nitrogen,
total
phosphorus
critical
factors
predictions.
contributes
development
advanced
predictive
models
HABs,
aiding
early
detection
proactive
management
strategies.
Environmental Research Communications,
Год журнала:
2024,
Номер
6(7), С. 075027 - 075027
Опубликована: Июль 1, 2024
Abstract
With
the
worldwide
growing
threat
of
flooding,
assessing
flood
risks
for
human
societies
and
associated
social
vulnerability
has
become
a
necessary
but
challenging
task.
Earlier
research
indicates
that
islands
usually
face
heightened
due
to
higher
population
density,
isolation,
oceanic
activities,
while
there
is
an
existing
lack
experience
in
island-focused
risk
under
complex
interactions
between
geography
socioeconomics.
In
this
context,
our
study
employs
high-resolution
hazard
data
principal
component
analysis
(PCA)
method
comprehensively
assess
exposure
Prince
Edward
Island
(PEI),
Canada,
where
limited
been
delivered
on
assessments.
The
findings
reveal
exposed
populations
are
closely
related
distribution
areas,
with
increasingly
severe
impact
from
current
future
climate
conditions,
especially
island’s
north
shore.
Exposed
buildings
exhibit
concentrated
at
different
levels
community
centers,
change
projected
significantly
worsen
building
compared
population,
possibly
urban
agglomeration
effect.
most
populated
cities
towns
show
highest
vulnerabilities
PEI,
results
reflect
relatively
less
economic
structure
islands.
Recommendations
management
coming
stage
include
necessity
particular
actions,
recognizing
centers
as
critical
sites
responses,
incorporating
hazards
into
planning
mitigate
impacts
continuous
urbanization
ecosystem
services
prevention.
Remote Sensing,
Год журнала:
2025,
Номер
17(4), С. 714 - 714
Опубликована: Фев. 19, 2025
Yu’nan
County
is
located
in
the
Pacific
Rim
geological
disaster-prone
area.
Frequent
landslides
are
an
important
cause
of
population,
property,
and
infrastructure
losses,
which
directly
threaten
sustainable
development
regional
social
economy.
Based
on
field
survey
data,
this
paper
employs
coefficient
variation
method
(CV)
improved
TOPSIS
model
(Kullback-Leibler-Technique
for
Order
Preference
by
Similarity
to
Ideal
Solution)
assess
vulnerability
landslide
disasters
182
administrative
villages
County.
Also,
it
conducts
a
ranking
comprehensive
analysis
their
levels.
Finally,
accuracy
evaluation
results
validated
applying
losses
incurred
from
per
unit
area
within
same
year.
The
indicate
significant
spatial
variability
across
County,
with
68
out
exhibiting
moderate
levels
or
higher.
This
suggests
high
risk
widespread
damage
potential
disasters.
Among
these,
Xincheng
village
has
highest
score,
while
Chongtai
lowest,
0.979
difference
vulnerabilities.
By
comparing
actual
landslides,
found
that
predicted
CV-KL-TOPSIS
more
consistent
results.
Furthermore,
among
ten
sub-factors,
population
density,
building
value,
road
value
contribute
most
significantly
overall
weight
0.269,
0.152,
0.105,
respectively,
suggesting
mountainous
areas
where
relatively
concentrated,
hazards
reflection
characteristics
local
economic
level.
framework
indicators
proposed
can
systematically
accurately
evaluate
landslide-prone
areas,
provide
reference
urban
planning
management
areas.
Advances in computer and electrical engineering book series,
Год журнала:
2025,
Номер
unknown, С. 247 - 272
Опубликована: Янв. 17, 2025
Effective
management
of
spatial
data
can
drive
green
innovation
by
identifying
environmental
challenges
such
as
air
and
water
quality,
deforestation,
soil
health,
climate
vulnerability.
Spatial
supports
pollution
detection
forest
cover
analysis,
along
with
sampling
for
erosion
assessment.
It
also
guide
targeted
initiatives
like
clean
efforts
sustainable
forestry.
Moreover,
it
optimize
resource
allocation
pinpointing
renewable
energy
sources
materials.
tailor
innovations
to
local
contexts,
inform
urban
planning,
enhance
waste
agriculture
practices,
monitor
impact.
Key
strategies
involve
collecting
high-quality
from
diverse
sources,
integrating
into
accessible
platforms,
ensuring
quality.
Collaboration
knowledge
sharing
data's
role
in
innovation.
Challenges
access,
ownership,
privacy
concerns
necessitate
solutions
open
policies,
clear
agreements,
capacity-building
programs.
Applied Sciences,
Год журнала:
2025,
Номер
15(9), С. 4824 - 4824
Опубликована: Апрель 26, 2025
Harmful
Algal
Blooms
(HABs),
predominantly
driven
by
cyanobacteria,
pose
significant
risks
to
water
quality,
public
health,
and
aquatic
ecosystems.
Lake
Erie,
particularly
its
western
basin,
has
been
severely
impacted
HABs,
largely
due
nutrient
pollution
climatic
changes.
This
study
aims
identify
key
physical,
chemical,
biological
drivers
influencing
HABs
using
a
multivariate
regression
analysis.
Water
quality
data,
collected
from
multiple
monitoring
stations
in
Erie
2013
2020,
were
analyzed
develop
predictive
models
for
chlorophyll-a
(Chl-a)
total
suspended
solids
(TSS).
The
correlation
analysis
revealed
that
particulate
organic
nitrogen,
turbidity,
carbon
the
most
influential
variables
predicting
Chl-a
TSS
concentrations.
Two
developed,
achieving
high
accuracy
with
R2
values
of
0.973
0.958
TSS.
demonstrates
robustness
techniques
identifying
HAB
drivers,
providing
framework
applicable
other
systems.
These
findings
will
contribute
better
prediction
management
strategies,
ultimately
helping
protect
resources
health.
