Agricultural Water Management,
Journal Year:
2024,
Volume and Issue:
293, P. 108690 - 108690
Published: Jan. 21, 2024
Sodium
hazard
poses
a
critical
threat
to
agricultural
production
globally
and
regionally
which
has
been
previously
predicted
from
ground
or
surface
water.
Monitoring
rainwater
quality
in
this
context
is
ignored
but
essential
for
water
management
central
Europe.
Our
study
focused
predict
sodium
adsorption
ratio
(SAR)
1985
2021
ten
ionic
species
of
(pH,
EC,
Cl-,
SO4−2,
NO3-,
NH4+,
Na+,
K+,
Mg2+,
Ca2+)
employing
four
machine
learning
(random
forest
(RF),
gaussian
process
regression
(GU),
random
subspace
(RSS),
artificial
neural
network-multilayer
perceptron
(ANN-MLP))
methods
at
three
stations
K-puszta
(KP),
Farkasfa
(FAK),
Nyirjes
(NYR)
Hungary,
Exploratory
data
analysis
was
performed
using
the
Mann-Kendall
test,
Pearson
correlation,
principal
component
(PCA).
Rainwater
composition
revealed
highest
percentage
SO4−2
ions
i.e.,
21
31%,
followed
by
10
15%
Na+
ions.
test
significant
(p
<
0.05)
increasing
trend
SAR
portraying
it
serious
limiting
production.
Machine
results
model
runs
all
algorithms
prediction
KP
station
proved
efficacy
ANN-MLP
as
superior
with
RMSE
range
0.02
0.05,
RF
0.14
0.19
scenario
2
(SC-2)
(Na+,
Ca2+).
Validation
best-selected
algorithm
(ANN-MLP)
also
low
0.08
0.05
both
FAK
NYR
stations,
respectively.
Hence,
efficiency
forecasting
proves
be
meticulous
tool
enhancing
practices
Central
Europe
resource
crop
future.
Sustainability,
Journal Year:
2023,
Volume and Issue:
15(9), P. 7593 - 7593
Published: May 5, 2023
The
present
study
was
carried
out
using
artificial
neural
network
(ANN)
model
for
predicting
the
sodium
hazardness,
i.e.,
adsorption
ratio
(SAR),
percent
(%Na)
residual,
Kelly’s
(KR),
and
residual
carbonate
(RSC)
in
groundwater
of
Pratapgarh
district
Southern
Rajasthan,
India.
This
focuses
on
verifying
suitability
water
irrigational
purpose,
wherein
more
decline
coupled
with
quality
problems
compared
to
other
areas
are
observed.
southern
part
Rajasthan
State
is
populated
as
rest
parts.
which
leads
industrialization,
urbanization,
evolutionary
changes
agricultural
production
region.
Therefore,
it
necessary
propose
innovative
methods
analyzing
(WQ)
use.
aims
develop
an
optimized
predict
hazardness
irrigation
purposes.
ANN
developed
‘nntool’
MATLAB
software.
trained
validated
ten
years
(2010–2020)
data.
An
L-M
3-layer
back
propagation
technique
adopted
architecture
a
reliable
accurate
irrigation.
Furthermore,
statistical
performance
indicators,
such
RMSE,
IA,
R,
MBE,
were
used
check
consistency
prediction
results.
model,
ANN4
(3-12-1),
(4-15-1),
ANN1
(4-5-1),
found
best
suited
SAR,
%Na,
RSC,
KR
indicators
district.
analysis
(3-12-1)
led
correlation
coefficient
=
1,
IA
RMS
0.14,
MBE
0.0050.
Hence,
proposed
provides
satisfactory
match
empirically
generated
datasets
observed
wells.
development
modeling
may
help
useful
planning
sustainable
management
resources
crop
plans
per
quality.
Geocarto International,
Journal Year:
2022,
Volume and Issue:
37(25), P. 10407 - 10433
Published: Jan. 26, 2022
The
use
of
contaminated
water
for
drinking
and
sanitary
purposes
can
be
detrimental
to
human
health.
In
this
article,
the
Human
Health
Risk
(HHRISK)
code
was
applied,
alongside
modified
heavy
metal
index
(MHMI),
synthetic
pollution
(SPI),
entropy-weighted
quality
(EWQI),
investigate
status,
ingestion,
dermal
health
risks
potentially
toxic
elements
(PTEs)
(Fe,
Zn,
Mn,
Pb,
Cr,
Ni)
in
resources
from
Umunya
area,
Nigeria.
Physicochemical
measurements
followed
standard
methods.
Results
MHMI,
SPI,
EWQI
revealed
that
about
60%
samples
had
low
were
considered
suitable
consumption,
while
40%
unsuitable.
Further,
cumulative
non-carcinogenic
risk
scores
indicated
pose
low-medium
high
child
adult
populations.
Contrarily,
results
carcinogenic
showed
6.67%
expose
users
risks,
whereas
93.33%
them
risks.
Although
there
are
agreements
between
both
populations
(regarding
risks),
it
is
worth
highlighting
children
higher.
Therefore,
study
area
more
vulnerable
Also,
due
ingestion
higher
than
contact.
Linear
regression
analysis
strong
agreement
indexical
models
While
artificial
neural
networks
multiple
linear
accurately
predicted
indices,
hierarchical
dendrograms
efficiently
classed
into
various
spatiotemporal
groups.
