Water,
Journal Year:
2024,
Volume and Issue:
17(1), P. 59 - 59
Published: Dec. 29, 2024
Irrigation
water
quality
is
crucial
for
sustainable
agriculture
and
environmental
health,
influencing
crop
productivity
ecosystem
balance
globally.
This
study
evaluates
the
performance
of
multiple
deep
learning
models
in
classifying
Water
Quality
Index
(IWQI),
addressing
challenge
accurate
prediction
by
examining
impact
increasing
input
complexity,
particularly
through
chemical
ions
derived
indices.
The
tested
include
convolutional
neural
networks
(CNN),
CNN-Long
Short-Term
Memory
(CNN-LSTM),
CNN-bidirectional
Long
(CNN-BiLSTM),
Gated
Recurrent
Unit
(CNN-BiGRUs).
Feature
selection
via
SHapley
Additive
exPlanations
(SHAP)
provided
insights
into
individual
feature
contributions
to
model
predictions.
objectives
were
compare
16
identify
most
effective
approach
IWQI
classification.
utilized
data
from
166
wells
Algeria’s
Naama
region,
with
70%
training
30%
testing.
Results
indicate
that
CNN-BiLSTM
outperformed
others,
achieving
an
accuracy
0.94
area
under
curve
(AUC)
0.994.
While
CNN
effectively
capture
spatial
features,
they
struggle
temporal
dependencies—a
limitation
addressed
LSTM
BiGRU
layers,
which
further
enhanced
bidirectional
processing
model.
importance
analysis
revealed
index
(qi)
qi-Na
was
significant
predictor
both
Model
15
(0.68)
(0.67).
qi-EC
showed
a
slight
decrease
importance,
0.19
0.18
between
models,
while
qi-SAR
qi-Cl
maintained
similar
levels.
Notably,
included
qi-HCO3
minor
score
0.02.
Overall,
these
findings
underscore
critical
role
sodium
levels
predictions
suggest
areas
enhancing
performance.
Despite
computational
demands
model,
results
contribute
development
robust
management,
thereby
promoting
agricultural
sustainability.
Water,
Journal Year:
2025,
Volume and Issue:
17(1), P. 82 - 82
Published: Jan. 1, 2025
Aquaculture
is
a
vital
contributor
to
global
food
security,
yet
maintaining
optimal
water
quality
remains
persistent
challenge,
particularly
in
resource-limited
rural
settings.
This
study
integrates
Internet
of
Things
(IoT)
technology,
Machine
Learning
(ML)
models,
and
the
Quantum
Approximate
Optimization
Algorithm
(QAOA)
enhance
monitoring
prediction
aquaculture.
IoT
sensors
continuously
measured
parameters
such
as
temperature,
dissolved
oxygen
(DO),
pH,
turbidity,
while
ML
models—including
Random
Forest—provided
high
accuracy
predictions
(R2
=
0.999,
RMSE
0.0998
mg/L).
The
integration
QAOA
reduced
model
training
time
by
50%,
enabling
rapid,
real-time
responses
changing
conditions.
Over
6000
corrective
interventions
were
conducted
during
study,
fish
survival
rates
above
90%
tropical
aquaculture
environments.
adaptable
system
designed
for
both
urban
settings,
using
low-cost
local
data
processing
constrained
environments
or
cloud-based
systems
analysis.
results
demonstrate
potential
IoT–ML–QAOA
mitigate
environmental
risks,
optimize
health,
support
sustainable
practices.
By
addressing
technological
infrastructural
constraints,
this
advances
management
contributes
security.
This
review
explores
the
application
of
machine
learning
in
predicting
and
optimizing
key
physicochemical
properties
deep
eutectic
solvents,
including
CO2
solubility,
density,
electrical
conductivity,
heat
capacity,
melting
temperature,
surface
tension,
viscosity.
By
leveraging
learning,
researchers
aim
to
enhance
understanding
customization
a
critical
step
expanding
their
use
across
various
industrial
research
domains.
The
integration
represents
significant
advancement
tailoring
solvents
for
specific
applications,
marking
progress
toward
development
greener
more
efficient
processes.
As
continues
unlock
full
potential
it
is
expected
play
an
increasingly
pivotal
role
revolutionizing
sustainable
chemistry
driving
innovations
environmental
technology.
Frontiers in Digital Health,
Journal Year:
2025,
Volume and Issue:
6
Published: Jan. 16, 2025
Fertility
preferences
refer
to
the
number
of
children
an
individual
would
like
have,
regardless
any
obstacles
that
may
stand
in
way
fulfilling
their
aspirations.
Despite
creation
and
application
numerous
interventions,
overall
fertility
rate
West
African
nations,
particularly
Nigeria,
is
still
high
at
5.3%
according
2018
Nigeria
Demographic
Health
Survey
data.
Hence,
this
study
aimed
predict
reproductive
age
women
using
state-of-the-art
machine
learning
techniques.
Secondary
data
analysis
from
recent
dataset
was
employed
feature
selection
identify
predictors
build
models.
Data
thoroughly
assessed
for
missingness
weighted
draw
valid
inferences.
Six
algorithms,
namely,
Logistic
Regression,
Support
Vector
Machine,
K-Nearest
Neighbors,
Decision
Tree,
Random
Forest,
eXtreme
Gradient
Boosting,
were
on
a
total
sample
size
37,581
Python
3.9
version.
Model
performance
accuracy,
precision,
recall,
F1-score,
area
under
receiver
operating
characteristic
curve
(AUROC).
Permutation
Gini
techniques
used
feature's
importance.
Forest
achieved
highest
with
accuracy
92%,
precision
94%,
recall
91%,
F1-score
AUROC
92%.
Factors
influencing
children,
group,
ideal
family
size.
Region,
contraception
intention,
ethnicity,
spousal
occupation
had
moderate
influence.
The
woman's
occupation,
education,
marital
status
lower
impact.
This
highlights
potential
analyzing
complex
demographic
data,
revealing
hidden
factors
associated
among
Nigerian
women.
In
conclusion,
these
findings
can
inform
more
effective
planning
promoting
sustainable
development
across
Nigeria.
