medRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 7, 2025
Abstract
Agricultural
injuries
remain
a
significant
occupational
hazard,
causing
substantial
human
and
economic
losses
worldwide.
This
study
investigates
the
prediction
of
agricultural
injury
severity
using
both
linear
ensemble
machine
learning
(ML)
models
applies
explainable
AI
(XAI)
techniques
to
understand
contribution
input
features.
Data
from
AgInjuryNews
(2015–2024)
was
preprocessed
extract
relevant
attributes
such
as
location,
time,
age,
safety
measures.
The
dataset
comprised
2,421
incidents
categorized
fatal
or
non-fatal.
Various
ML
models,
including
Naïve
Bayes
(NB),
Decision
Tree
(DT),
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Gradient
Boosting
(GB),
were
trained
evaluated
standard
performance
metrics.
Ensemble
demonstrated
superior
accuracy
recall
compared
with
XGBoost
achieving
100%
for
injuries.
However,
all
faced
challenges
in
predicting
non-fatal
due
class
imbalance.
SHAP
analysis
provided
insights
into
feature
importance,
gender,
time
emerging
most
influential
predictors
across
models.
research
highlights
effectiveness
while
emphasizing
need
balanced
datasets
XAI
actionable
insights.
findings
have
practical
implications
enhancing
guiding
policy
interventions.
Highlights
analyzed
(2015–
2024)
utilized
predict
severity,
focusing
on
outcomes.
Forest,
outperformed
recall,
especially
injuries,
although
predictions
imbalance
observed.
Key
identified
through
included
providing
interpretable
factors
influencing
severity.
integration
enhanced
transparency
predictions,
enabling
stakeholders
prioritize
targeted
interventions
effectively.
potential
combining
improve
practices
provides
foundation
addressing
data
future
studies.
Graphical
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
147, P. 109959 - 109959
Published: Jan. 30, 2023
Urban
flood
is
one
of
the
most
frequent
and
deadly
natural
disasters
in
world,
seriously
affecting
urban
sustainability
people's
well-being
China.
As
largest
developing
country
China
urgently
needs
to
improve
its
resilience.
Previous
studies
related
resilience
are
mostly
focused
on
assessment
method
simulation.
However,
few
directly
aim
reveal
influencing
factors
their
inner
relationships.
In
order
make
a
significant
contribution
long-term
improvement
context
global
climate
change
urbanization,
it
crucial
explore
mechanisms
This
study
aims
identify
key
interactions
To
this
end,
conceptual
framework
based
Pressure-State-Response
model
Social-Economic-Natural
Complex
Ecosystem
theory
(PSR-SENCE
model)
established
24
identified
within
three
dimensions.
The
relationships
between
tested
using
fuzzy-DEMATEL
method.
results
that
pressure
response
dimensions
have
greater
impact
whole
system,
while
state
dimension
more
influenced
by
other
two
14
critical
factors,
with
four
detailed
influence
paths
discussed
among
different
Accordingly,
implications
for
improving
paths.
provides
theoretical
basis
approach
how
proposes
specific
implications.
International Journal of Disaster Risk Reduction,
Journal Year:
2023,
Volume and Issue:
93, P. 103751 - 103751
Published: May 19, 2023
Urban
pluvial
floods
or
rainfall-driven
are
often
misrepresented
as
nuisance
and
tend
to
receive
limited
attention.
In
India,
recent
urban
have
affected
population
displacement,
damaged
infrastructure,
impacted
means
of
livelihood.
this
paper,
we
describe
the
current
state
flood
research
in
focusing
on
how
scholarly
community
approaches
causes,
impacts,
mitigation
strategies
settings.
This
systematic
literature
review
academic
databases
SCOPUS,
Web
Science,
Science
Direct,
Google
Scholar
asks:
1)
context
do
define
flooding?
2)
What
factors
cause
flooding
India?
3)
impacts
and4)
should
be
adopted
cope
with
floods?
Our
close
62
articles
finds
that
India
attributed
extreme
rainfall
(n
=
51),
development
44),
topography
34),
drainage
33),
waste
13),
management
11),
soil
type
7).
We
categorize
reported
such
direct
31)
indirect
14)
suggested
proactive
57),
reactive
6),
recovery
10).
provides
a
summary
suggests
new
directions
for
future
research.
International Journal of River Basin Management,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 18
Published: Feb. 13, 2024
Given
the
growing
climate
variability,
quantifying
droughts
has
gained
significant
importance,
particularly
in
agriculturally
concentrated
areas
such
as
Iowa.
This
study
presents
a
novel
approach
for
evaluating
risk
of
agricultural
drought,
which
combines
geospatial
methods
with
fuzzy
logic
algorithm.
The
integrates
diverse
array
meteorological,
physical,
and
social
factors,
yielding
more
comprehensive
nuanced
understanding
impacts
drought.
covered
sector
within
Corn
Belt
region
Iowa
formulated
maps
illustrating
vulnerability
drought
timeframe
spanning
from
2015
to
2021.
illustrate
progress
analysis,
fully
representing
spatial
temporal
dimensions
uniqueness
this
is
ascribed
its
methodological
framework,
thorough
assessment
prior
research
inform
assignment
weights
parameters
logic-based
index.
findings
demonstrate
notable
increase
proportion
Iowa's
land
area
classified
at
a'very
high'
risk,
rising
0.66%
5.39%
2018.
upward
trend
suggests
an
escalating
susceptibility
conditions.
Mid-Iowa
western
portion
state
exhibited
increased
'high'
'extremely
threats
during
period.
accuracy
our
was
validated
using
Kappa
coefficient
75%.
indicator
potential
be
utilized
context
mitigation
program
monitoring.
Moreover,
methodology
can
modified
implementation
geographical
across
globe.
