A systematic review of trustworthy artificial intelligence applications in natural disasters
Computers & Electrical Engineering,
Год журнала:
2024,
Номер
118, С. 109409 - 109409
Опубликована: Июнь 29, 2024
Artificial
intelligence
(AI)
holds
significant
promise
for
advancing
natural
disaster
management
through
the
use
of
predictive
models
that
analyze
extensive
datasets,
identify
patterns,
and
forecast
potential
disasters.
These
facilitate
proactive
measures
such
as
early
warning
systems
(EWSs),
evacuation
planning,
resource
allocation,
addressing
substantial
challenges
associated
with
This
study
offers
a
comprehensive
exploration
trustworthy
AI
applications
in
disasters,
encompassing
management,
risk
assessment,
prediction.
research
is
underpinned
by
an
review
reputable
sources,
including
Science
Direct
(SD),
Scopus,
IEEE
Xplore
(IEEE),
Web
(WoS).
Three
queries
were
formulated
to
retrieve
981
papers
from
earliest
documented
scientific
production
until
February
2024.
After
meticulous
screening,
deduplication,
application
inclusion
exclusion
criteria,
108
studies
included
quantitative
synthesis.
provides
specific
taxonomy
disasters
explores
motivations,
challenges,
recommendations,
limitations
recent
advancements.
It
also
overview
techniques
developments
using
explainable
artificial
(XAI),
data
fusion,
mining,
machine
learning
(ML),
deep
(DL),
fuzzy
logic,
multicriteria
decision-making
(MCDM).
systematic
contribution
addresses
seven
open
issues
critical
solutions
essential
insights,
laying
groundwork
various
future
works
trustworthiness
AI-based
management.
Despite
benefits,
persist
In
these
contexts,
this
identifies
several
unused
used
areas
disaster-based
theory,
collects
ML,
DL
techniques,
valuable
XAI
approach
unravel
complex
relationships
dynamics
involved
utilization
fusion
processes
related
Finally,
extensively
analyzed
ethical
considerations,
bias,
consequences
AI.
Язык: Английский
Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions
Remote Sensing,
Год журнала:
2025,
Номер
17(5), С. 774 - 774
Опубликована: Фев. 23, 2025
Wheat
(Triticum
aestivum
L.)
is
one
of
the
world’s
primary
food
crops,
and
timely
accurate
yield
prediction
essential
for
ensuring
security.
There
has
been
a
growing
use
remote
sensing,
climate
data,
their
combination
to
estimate
yields,
but
optimal
indices
time
window
wheat
in
arid
regions
remain
unclear.
This
study
was
conducted
(1)
assess
performance
widely
recognized
sensing
predict
at
different
growth
stages,
(2)
evaluate
predictive
accuracy
machine
learning
models,
(3)
determine
appropriate
period
regions,
(4)
impact
parameters
on
model
accuracy.
The
vegetation
indices,
due
proven
effectiveness,
used
this
include
Normalized
Difference
Vegetation
Index
(NDVI),
Enhanced
(EVI),
Atmospheric
Resistance
(ARVI).
Moreover,
four
viz.
Decision
Trees
(DTs),
Random
Forest
(RF),
Gradient
Boosting
(GB),
Bagging
(BTs),
were
evaluated
region.
whole
divided
into
three
windows:
tillering
grain
filling
(December
15–March),
stem
elongation
(January
heading
(February–March
15).
developed
Google
Earth
Engine
(GEE),
combining
data.
results
showed
that
RF
with
ARVI
could
accurately
maturity
stages
an
R2
>
0.75
error
less
than
10%.
stage
identified
as
regions.
While
delivered
best
results,
GB
EVI
slightly
lower
precision
still
outperformed
other
models.
It
concluded
multisource
data
models
promising
approach
Язык: Английский
Unveiling the thermal impact of land cover transformations in Khuzestan province through MODIS satellite remote sensing products
Paddy and Water Environment,
Год журнала:
2024,
Номер
22(4), С. 503 - 520
Опубликована: Июнь 5, 2024
Язык: Английский
Interdecadal Variations in Agricultural Drought Monitoring Using Land Surface Temperature and Vegetation Indices: A Case of the Amahlathi Local Municipality in South Africa
Sustainability,
Год журнала:
2024,
Номер
16(18), С. 8125 - 8125
Опубликована: Сен. 18, 2024
Agricultural
droughts
in
South
Africa,
particularly
the
Amahlathi
Local
Municipality
(ALM),
significantly
impact
socioeconomic
activities,
sustainable
livelihoods,
and
ecosystem
services,
necessitating
urgent
attention
to
improved
resilience
food
security.
The
study
assessed
interdecadal
drought
severity
duration
Amahlathi’s
agricultural
potential
zone
from
1989
2019
using
various
vegetation
indicators.
Landsat
time
series
data
were
used
analyse
land
surface
temperature
(LST),
soil-adjusted
index
(SAVI),
normalized
difference
(NDVI),
standardized
precipitation
(SPI).
utilised
GIS-based
weighted
overlay,
multiple
linear
regression
models,
Pearson’s
correlation
analysis
assess
correlations
between
LST,
NDVI,
SAVI,
SPI
response
extent.
results
reveal
a
consistent
negative
LST
NDVI
ALM,
with
an
increase
(R2
=
0.9889)
temperature.
accuracy
dry
areas
increased
55.8%
2019,
despite
dense
high
average
of
40.12
°C,
impacting
water
availability,
land,
local
ecosystems.
shows
ALM
increasing
since
2019.
SAVI
indicates
slight
improvement
overall
health
0.18
0.25
2009,
but
decrease
0.21
at
12
24
months
that
severely
impacted
cover
2014
notable
recovery
during
wet
periods
1993,
2000,
2003,
2006,
2008,
2013,
possibly
due
temporary
relief.
findings
can
guide
provincial
monitoring
early
warning
programs,
enhancing
resilience,
productivity,
especially
farming
communities.
Язык: Английский
Enhancing object detection in low-light conditions with adaptive parallel networks
Journal of Electronic Imaging,
Год журнала:
2025,
Номер
34(01)
Опубликована: Янв. 11, 2025
In
low-light
conditions,
object
detection
algorithms
suffer
from
reduced
accuracy
due
to
factors
such
as
noise
and
insufficient
information.
Current
solutions
often
involve
a
two-stage
process:
first,
improving
image
illumination
then
performing
detection.
However,
this
method
has
limitations
these
networks
work
independently.
To
address
this,
we
propose
parallel
algorithm
for
environments.
Our
approach
simultaneously
encodes
features
using
both
an
enhancement
network
network.
This
innovative
design
allows
adapt
each
other,
feature
adaptability
We
enhance
adaptive
learning
efficiency
by
introducing
novel
mutual
feedback
mechanism,
which
dynamically
adjusts
the
weights
of
two
networks,
thereby
enhancing
network's
capacity
encode
object-related
information
in
conditions.
Experiments
were
conducted
on
real-world
synthetic
datasets.
On
dataset,
proposed
outperformed
original
network,
achieving
improvements
4.76%
[email protected],
12.12%
[email protected]:0.95,
8.4%
F1-score.
demonstrated
gains
9.67%,
9.75%,
10.6%
F1-score,
respectively.
These
experimental
results
indicate
that
significantly
enhances
performance
under
Язык: Английский
Digital technologies for water use and management in agriculture: Recent applications and future outlook
Agricultural Water Management,
Год журнала:
2025,
Номер
309, С. 109347 - 109347
Опубликована: Фев. 2, 2025
Язык: Английский
Systematic Review on the Application of Nanotechnology and Artificial Intelligence in Agricultural Economics
LatIA,
Год журнала:
2025,
Номер
3, С. 322 - 322
Опубликована: Май 7, 2025
The
convergence
of
nanotechnology
and
artificial
intelligence
(AI)
represents
a
transformative
force
in
agricultural
economics,
offering
innovative
solutions
to
longstanding
challenges
such
as
productivity
inefficiencies,
environmental
degradation,
unsustainable
resource
use.
This
study
presents
systematic
literature
review
(SLR)
aimed
at
synthesising
theoretical
frameworks,
applications,
economic
implications
associated
with
these
technologies
agriculture.
A
structured
search
strategy
was
developed
using
Boolean
operators
combine
key
terms
related
nanotechnology,
AI,
machine
learning.
Comprehensive
searches
were
conducted
across
six
academic
databases—Springer,
IEEE
Xplore,
ACM,
Science
Direct,
Wiley,
Google
Scholar—complemented
by
manual
snowballing
techniques.
From
an
initial
pool
840
records,
55
studies
met
the
inclusion
criteria
after
rigorous
screening
eligibility
assessment.
Findings
indicate
that
enhances
nutrient
delivery,
pest
control,
crop
monitoring
through
nanosensors
nano-fertilisers,
while
AI
facilitates
data-driven
decision-making,
yield
prediction,
optimisation
precision
farming.
Despite
promising
results,
high
investment,
technological
complexity,
limited
access
for
smallholder
farmers
remain
significant.
concludes
integration
can
improve
efficiency,
viability,
sustainability.
However,
targeted
investments,
capacity-building,
interdisciplinary
collaboration
are
essential
bridge
gap
between
innovation
implementation
developing
economies.
Язык: Английский
A combined drought index for monitoring and assessment of drought severity over India by integrating CHIRPS, MODIS and GRACE data
Environment Development and Sustainability,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 18, 2024
Язык: Английский
Developing Framework for Implementing Total Quality Management (TQM) in Sustainable Industrialized Building System (IBS) in Construction Projects
Sustainability,
Год журнала:
2024,
Номер
16(23), С. 10399 - 10399
Опубликована: Ноя. 27, 2024
The
construction
sector
is
increasingly
shifting
towards
sustainable
and
efficient
methodologies,
with
the
industrialized
building
system
(IBS)
playing
a
pivotal
role
in
this
transformation.
Despite
this,
adoption
of
total
quality
management
(TQM)
IBS
projects
faces
significant
challenges,
including
lack
comprehensive
understanding
TQM
standards
resistance
to
change
within
industry.
This
study
addresses
these
gaps
by
developing
framework
for
implementing
projects.
objective
enhance
project
sustainability
addressing
critical
issues
such
as
limited
stakeholder
awareness
opposition
adoption.
Using
qualitative
methodology
rooted
phenomenology,
explores
lived
experiences
key
stakeholders
involved
projects,
managers,
professionals,
government
officials.
Data
were
collected
through
in-depth
interviews
capture
their
perspectives
on
integration
context.
findings
highlight
crucial
fostering
continuous
improvement,
enhancing
collaboration,
ensuring
adherence
throughout
lifecycle.
proposed
incorporates
essential
principles
process
optimization,
employee
engagement,
customer
focus,
providing
structured
approach
overcoming
barriers
effective
implementation.
Furthermore,
promotes
reducing
waste
improving
energy
efficiency
offers
valuable
insights
policymakers,
industry
stakeholders,
presenting
practical
solutions
improve
construction.
Leadership,
cultural
transformation,
improvement
are
identified
factors
successful
integration,
ultimately
leading
more
processes
Язык: Английский
Delineation of Groundwater potential zone using Geospatial and AHP techniques in Ken River Basin (KRB) in Central India
Discover Water,
Год журнала:
2024,
Номер
4(1)
Опубликована: Авг. 23, 2024
Groundwater
is
the
most
salient
and
utilitarian
water
resource
for
living
organisms.
However,
major
parts
of
Ken
River
Basin
(KRB)
in
Central
India
are
grappling
with
overexploitation
groundwater
resources,
primarily
due
to
extensive
agricultural
activities,
raising
problems
achieving
Sustainable
Development
Goals
(SDGs)
2
6.
This
study
focused
on
delineating
potential
zones
(GPZ)
by
employing
remote
sensing
GIS-based
thematic
datasets,
complemented
application
Analytic
Hierarchy
Process
(AHP).
The
layers
consisting
geomorphology,
precipitation,
geology,
soil
texture,
lineament
density,
slope,
LULC,
drainage
density
were
considered
further
weights
allocated
respect
their
storing
capacity
characteristics
occurrences
develop
GPZs.
generated
classifying
overlayed
maps
into
four
categories
namely,
very
low,
moderate,
high.
key
findings
indicated
that
13.84%,
62.34%,
23.37%,
45%
areas
found
under
high,
low
GPZs
respectively.
Furthermore,
zonation
map
was
validated
48
boreholes'
yield,
which
revealed
a
noteworthy
77.08%
borewells
concurrence
predicted
zones.
Area
Under
Receiver
Operating
Characteristic
(AUROC)
curve
showcased
commendable
70.1%
accuracy
using
ROC
curve.
These
results
highly
beneficial
formulating
sustainable
management
plans
policies,
contributing
towards
attainment
targets
outlined
SDGs
6,
particularly
regions
resembling
KRB.
Язык: Английский