AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges
LatIA,
Journal Year:
2025,
Volume and Issue:
3, P. 332 - 332
Published: March 25, 2025
Climate
models
are
fundamental
for
predicting
future
climate
conditions
and
guiding
mitigation
adaptation
strategies.
This
study
aims
to
enhance
the
accuracy
reliability
of
modeling
by
integrating
artificial
intelligence
(AI)
techniques
validation
uncertainty
mapping.
AI-driven
approaches,
including
machine
learning-based
parameterization,
ensemble
simulations,
probabilistic
modeling,
offer
improvements
in
model
precision,
quality
assurance,
quantification.
A
systematic
review
methodology
was
applied,
selecting
peer-reviewed
studies
from
2000
2023
that
focused
on
validation,
estimation.
Data
sources
included
observational
records,
satellite
measurements,
global
reanalysis
datasets.
The
analyzed
key
methodologies
used
improving
accuracy,
statistical
downscaling
deep
prediction
frameworks.
Findings
indicate
AI-enhanced
significantly
improve
projections
refining
enhancing
bias
correction,
optimizing
Machine
learning
applications
facilitate
more
accurate
predictions
meteorological
phenomena,
temperature
precipitation
variability.
However,
challenges
remain
addressing
biases,
inter-model
inconsistencies,
computational
limitations.
concludes
advancements
provide
critical
reliability,
yet
ongoing
refinements
necessary
address
persistent
uncertainties.
Enhancing
datasets,
techniques,
strengthening
frameworks
will
be
essential
reducing
uncertainty.
Effective
communication
outputs,
mapping,
is
crucial
supporting
informed
policy
decisions.
a
rapidly
evolving
field,
continuous
innovation
predictive
resilience
Language: Английский
Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI
LatIA,
Journal Year:
2025,
Volume and Issue:
3, P. 318 - 318
Published: May 8, 2025
Sub-Saharan
Africa
(SSA)
faces
persistent
food
insecurity
due
to
low
agricultural
productivity,
limited
access
modern
technologies,
and
growing
climate
variability.
This
study
explores
the
transformative
potential
of
Artificial
Intelligence
(AI)
enhance
systems
across
SSA.
The
objective
is
assess
how
AI
applications—such
as
machine
learning,
remote
sensing,
big
data
analytics—can
address
systemic
inefficiencies
in
cereal
crop
production,
with
a
focus
on
barley,
millet,
sorghum.
Using
systematic
review
approach
aligned
PRISMA
guidelines,
literature
from
2015–2025
was
analyzed
multiple
databases
identify
empirical
studies
models
related
SSA
agriculture.
Results
reveal
that
can
significantly
improve
monitoring,
yield
forecasting,
resource
optimization.
However,
adoption
barriers
such
inadequate
infrastructure,
financial
constraints,
digital
divide
persist.
concludes
while
holds
significant
promise,
its
success
depends
inclusive
policies,
capacity
building,
localized
governance.
It
recommends
interdisciplinary
research,
investment
rural
participatory
innovation
frameworks
empower
smallholder
farmers
ensure
equitable
deployment.
provides
roadmap
for
integrating
into
resilience,
security.
Language: Английский