medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 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
Environmental Research Communications,
Год журнала:
2024,
Номер
6(10), С. 102003 - 102003
Опубликована: Окт. 1, 2024
Abstract
Hydrometeorological
disasters,
including
floods
and
droughts,
have
intensified
in
both
frequency
severity
recent
years.
This
trend
underscores
the
critical
role
of
timely
monitoring,
accurate
forecasting,
effective
warning
systems
facilitating
proactive
responses.
Today’s
information
offer
a
vast
intricate
mesh
data,
encompassing
satellite
imagery,
meteorological
metrics,
predictive
modeling.
Easily
accessible
to
general
public,
these
cyberinfrastructures
simulate
potential
disaster
scenarios,
serving
as
invaluable
aids
decision-making
processes.
review
collates
key
literature
on
water-related
systems,
underscoring
transformative
impact
emerging
Internet
technologies.
These
advancements
promise
enhanced
flood
drought
timeliness
greater
preparedness
through
improved
management,
analysis,
visualization,
data
sharing.
Moreover,
aid
hydrometeorological
predictions,
foster
development
web-based
educational
platforms,
support
frameworks,
digital
twins,
metaverse
applications
contexts.
They
further
bolster
scientific
research
development,
enrich
climate
change
vulnerability
strengthen
associated
cyberinfrastructures.
article
delves
into
prospective
developments
realm
natural
pinpointing
primary
challenges
gaps
current
highlighting
intersections
with
future
artificial
intelligence
solutions.
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 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
