International Journal of Disaster Risk Reduction,
Journal Year:
2024,
Volume and Issue:
108, P. 104503 - 104503
Published: April 23, 2024
Floods
are
a
widespread
and
damaging
natural
phenomenon
that
causes
harm
to
human
lives,
resources,
property
has
agricultural,
eco-environmental,
economic
impacts.
Therefore,
it
is
crucial
perform
flood
susceptibility
mapping
(FSM)
identify
susceptible
zones
mitigate
reduce
damage.
This
study
assessed
the
damage
caused
by
2022
flash
in
Sindh
identified
flood-susceptible
based
on
frequency
ratio
(FR)
analytical
hierarchy
process
(AHP)
models.
Flood
inventory
maps
were
generated,
containing
150
sampling
points,
which
manually
selected
from
Landsat
imagery.
The
points
split
into
70%
for
training
30%
validating
results.
Furthermore,
fourteen
conditioning
factors
considered
analysis
developing
FSM.
final
FSM
categorized
five
zones,
representing
levels
very
low
high.
results
areas
under
high
Ghotki
(FR
4.42%
AHP
5.66%),
Dadu
21.40%
21.29%),
Sanghar
6.81%
6.78%).
Ultimately,
accuracy
was
evaluated
using
receiver
operating
characteristics
area
curve
method,
resulting
82%,
83%),
91%,
90%),
96%,
95%).
enhances
scientific
understanding
of
impacts
across
diverse
regions
emphasizes
importance
accurate
informed
decision-making.
findings
provide
valuable
insights
supportive
policymakers,
agricultural
planners,
stakeholders
engaged
risk
management
adverse
consequences
floods.
Geocarto International,
Journal Year:
2024,
Volume and Issue:
39(1)
Published: Jan. 1, 2024
The
present
research
is
conducted
in
the
southern
region
of
Khyber
Pakhtunkhwa,
Pakistan,
to
identify
groundwater
potential
zones
(GWPZ).
We
used
three
models
including
Weight
Evidence
(WOE),
Frequency
Ratio
(FR),
and
Information
Value
(IV)
with
twelve
parameters
(elevation,
slope,
aspect,
curvature,
drainage
network,
LULC,
precipitation,
geology,
Lineament,
NDVI,
road,
soil
texture,
that
have
been
prepared
integrated
into
ArcGIS
10.8.
reliability
applied
models'
results
was
validated
using
Area
Under
Receiver
Operating
Characteristics
(AUROC).
GWPZ
were
reclassified
five
classes,
i.e.
very
low,
medium,
high,
high
zone.
area
occupied
by
mentioned
classes
WOE
are
low
(10.14%),
(19.58%),
medium
(26.75%),
(27.10%),
(16.40%),
while
FR
(20.93%),
(32.38%),
(18.92%),
(13.13%),
(14.61%)
IV
(14.41%),
(17.17%),
(29.01%),
(25.85%),
High
(13.53%).
Success
Rate
Curve
WOE,
FR,
0.86,
0.91,
0.87,
Predicted
values
0.89,
0.93,
0.90,
respectively.
revealed
all
statistical
performed
well
delineate
GWPZ.
However,
use
technique
strongly
encouraged
evaluate
GWPZ,
its
findings
especially
useful
for
managing
resources
urban
planning.
Our
approaches
assessing
mapping
can
be
any
similar
scenarios
recommended
as
a
helpful
tool
policymakers
manage
groundwater.
Climate Risk Management,
Journal Year:
2024,
Volume and Issue:
45, P. 100630 - 100630
Published: Jan. 1, 2024
Monitoring
drought
in
semi-arid
regions
due
to
climate
change
is
of
paramount
importance.
This
study,
conducted
Morocco's
Upper
Drâa
Basin
(UDB),
analyzed
data
spanning
from
1980
2019,
focusing
on
the
calculation
indices,
specifically
Standardized
Precipitation
Index
(SPI)
and
Evapotranspiration
(SPEI)
at
multiple
timescales
(1,
3,
9,
12
months).
Trends
were
assessed
using
statistical
methods
such
as
Mann-Kendall
test
Sen's
Slope
estimator.
Four
significant
machine
learning
(ML)
algorithms,
including
Random
Forest,
Voting
Regressor,
AdaBoost
K-Nearest
Neighbors
evaluated
predict
SPEI
values
for
both
three
12-month
periods.
The
algorithms'
performance
was
measured
indices.
study
revealed
that
distribution
within
UDB
not
uniform,
with
a
discernible
decreasing
trend
values.
Notably,
four
ML
algorithms
effectively
predicted
specified
demonstrated
highest
Nash-Sutcliffe
Efficiency
(NSE)
values,
ranging
0.74
0.93.
In
contrast,
algorithm
produced
range
0.44
0.84.
These
research
findings
have
potential
provide
valuable
insights
water
resource
management
experts
policymakers.
However,
it
imperative
enhance
collection
methodologies
expand
measurement
sites
improve
representativeness
reduce
errors
associated
local
variations.
Case Studies in Thermal Engineering,
Journal Year:
2024,
Volume and Issue:
55, P. 104117 - 104117
Published: Feb. 12, 2024
According
to
the
Global
Climate
Risk
Index,
Pakistan
is
fifth
most
vulnerable
nation
in
world
climate
change.
The
growing
phenomena
of
change
and
global
warming
have
increased
on
a
worldwide
level.
To
combat
effects
change,
transition
sustainable
transportation
system
essential.
Developed
countries
evaluated
costs
benefits
such
transition.
However,
developing
like
rarely
investigated
this
matter
thoroughly.
So,
context,
paper
case
study
analyzing
transport
sector
Punjab-Pakistan
achieve
some
targets
for
transportation.
analysis
carried
out
by
using
energy
model
Low
Emission
Analysis
Platform
(LEAP)
from
2019
2050.
Three
scenarios
are
made,
i.e.,
Business
as
Usual
Scenario
(BAUS)
following
current
policies,
Efficient
Combustion
(ECS),
Electrical
Vehicle
(EVS)
figure
environmental
social
costs.
It
concluded
that
2050,
ECS
EVS
will
reduce
carbon
dioxide
emissions
21.6
18.5
million
metric
tons
equivalent,
compared
Business-as-Usual
Scenario.
These
savings
terms
cost
be
$
157.1
134.6
Electric
This
research
may
help
find
suitable
policy
decisions
at
provincial
level
enhance
sustainability
increasing
share
electric
vehicles
Punjab,
results
replicated
whole
country
South
Asia.
Groundwater for Sustainable Development,
Journal Year:
2024,
Volume and Issue:
25, P. 101137 - 101137
Published: March 13, 2024
Groundwater
resources
in
arid
regions
play
a
vital
role
meeting
water
demands;
however,
they
are
facing
rapid
depletion
due
to
unsustainable
exploitation
practices,
exacerbated
by
climate
change.
Floods
can
present
unique
opportunity
for
restoring
groundwater
levels
and
mitigating
saltwater
intrusion
into
aquifers.
The
use
of
properly
managed
floodwater
aquifer
recharge
offers
dual
advantage
maximizing
the
potential
floods
as
valuable
resource,
while
minimizing
their
negative
impacts.
In
this
work,
we
applied
GIS-based
Multi-Criteria
Decision-Making
(MCDM)
method,
namely
Analytic
Hierarchy
Process
(AHP),
delineate
flood
susceptible
zones
Qatar,
considering
several
influential
topographical,
hydrological,
environmental,
anthropological
criteria.
maps
susceptibility
were
validated
using
recent
flooding
events
existing
wells
data,
respectively.
Sensitivity
analysis
was
conducted
on
both
variables
further
assess
accuracy.
overlay
two
suggests
that
approximately
64%
Qatar
peninsula
presents
medium
excellent
suitability
floodwater.
areas
best
suited
floodwater-based
intervention
northern
coastal
peninsula,
urban
southwestern
area
less
suitable.
This
study
provides
decision-makers
with
spatially
explicit
information
be
targeted
projects
well
recommendations
technical,
economic,
regulatory
considerations
require
additional
investigation.
approach
employed
effectively
similar
flood-prone
is
adaptable
diverse
contexts.
European Journal of Remote Sensing,
Journal Year:
2023,
Volume and Issue:
56(1)
Published: Sept. 5, 2023
The
main
objectives
of
this
study
are
(1)
to
compare
several
machine
learning
models
predict
county-level
corn
yield
in
the
area
and
(2)
feasibility
for
in-season
prediction.
We
acquired
remotely
sensed
vegetation
indices
data
from
moderate
resolution
imaging
spectroradiometer
using
Google
Earth
Engine
(GEE).
Vegetation
a
span
15
years
(2006–2020)
were
processed
downloaded
GEE
months
corresponding
crop
growth
(April–October).
compared
nine
yield.
Furthermore,
we
analyzed
prediction
performance
top
three
models.
results
show
that
partial
least
square
regression
(PLSR)
outperformed
other
by
achieving
highest
training
testing
performance.
area's
PLSR,
support
vector
(SVR)
ridge
regression.
For
prediction,
SVR
model
performed
comparatively
well
R2
=
0.875.
can
both
(best
0.875)
end-of-season
0.861)
with
satisfactory
indicate
remote
sensing
be
used
before
harvest
decent
This
provide
useful
insights
terms
food
security
early
decision
making
related
climate
change
impacts
on
security.
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: Nov. 14, 2023
Satellite
remote
sensing
is
widely
being
used
by
the
researchers
and
geospatial
scientists
due
to
its
free
data
access
for
land
observation
agricultural
activities
monitoring.
The
world
suffering
from
food
shortages
dramatic
increase
in
population
climate
change.
Various
crop
genotypes
can
survive
harsh
climatic
conditions
give
more
production
with
less
disease
infection.
Remote
play
an
essential
role
genotype
identification
using
computer
vision.
In
many
studies,
different
objects,
crops,
cover
classification
done
successfully,
while
still
a
gray
area.
Despite
importance
of
planning,
significant
method
has
yet
be
developed
detect
varieties
yield
multispectral
radiometer
data.
this
study,
three
wheat
(Aas-'2011',
'Miraj-'08',
'Punjnad-1)
fields
are
prepared
investigation
radio
meter
band
properties.
Temporal
(every
15
days
height
10
feet
covering
5
circle
one
scan)
collected
efficient
Radio
Meter
(MSR5
five
bands).
Two
hundred
samples
each
acquired
manually
labeled
accordingly
training
supervised
machine
learning
models.
To
find
strength
features
(five
bands),
Principle
Component
Analysis
(PCA),
Linear
Discriminant
(LDA),
Nonlinear
Discernment
(NDA)
performed
besides
models
Extra
Tree
Classifier
(ETC),
Random
Forest
(RF),
Support
Vector
Machine
(SVM),
Decision
(DT),
Logistic
Regression
(LR),
k
Nearest
Neighbor
(KNN)
Artificial
Neural
Network
(ANN)
detailed
configuration
settings.
ANN
random
forest
algorithm
have
achieved
approximately
maximum
accuracy
97%
96%
on
test
dataset.
It
recommended
that
digital
policymakers
agriculture
department
use
RF
identify
at
farmer's
research
centers.
These
findings
precision
management
specific
optimized
resource
efficiency.
Heliyon,
Journal Year:
2023,
Volume and Issue:
10(1), P. e23151 - e23151
Published: Dec. 13, 2023
Dengue
is
one
of
Pakistan's
major
health
concerns.
In
this
study,
we
aimed
to
advance
our
understanding
the
levels
knowledge,
attitudes,
and
practices
(KAPs)
in
Fever
(DF)
hotspots.
Initially,
at-risk
communities
were
systematically
identified
via
a
well-known
spatial
modeling
technique,
named,
Kernel
Density
Estimation,
which
was
later
targeted
for
household-based
cross-sectional
survey
KAPs.
To
collect
data
on
sociodemographic
KAPs,
random
sampling
utilized
(n
=
385,
5
%
margin
error).
Later,
association
different
demographics
(characteristics),
attitude
factors—potentially
related
poor
preventive
assessed
using
bivariate
(individual)
multivariable
(model)
logistic
regression
analyses.
Most
respondents
(>90
%)
fever
as
sign
DF;
headache
(73.8
%),
joint
pain
(64.4
muscular
(50.9
behind
eyes
(41.8
bleeding
(34.3
skin
rash
(36.1
relatively
less.
Regression
results
showed
significant
associations
knowledge/attitude
with
practices;
dengue
vector
(odds
ratio
[OR]
3.733,
95
confidence
interval
[CI
]
2.377–5.861;
P
<
0.001),
DF
symptoms
(OR
3.088,
CI
1.949–4.894;
transmission
1.933,
1.265–2.956;
0.002),
3.813,
1.548–9.395;
0.004).
Moreover,
education
level
stronger
analysis
strongest
independent
factor
(illiterate:
adjusted
OR
6.833,
2.979–15.672;
0.001)
primary
(adjusted
4.046,
1.997–8.199;
0.001).
This
situation
highlights
knowledge
gaps
within
urban
communities,
particularly
signs/symptoms.
The
also
plays
substantial
role
control,
observed
where
more
prevalent
among
illiterate
less
educated
respondents.
