Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: July 19, 2023
Abstract
In
this
study,
machine
learning
classifiers
are
integrated
with
the
geostatistical
analyses.
The
data
extracted
from
surface
maps
derived
ordinary
kriging
were
passed
onto
ML
algorithms,
resulting
in
prediction
accuracies
of
95%
(Gradient
Boosting
Classifier)
for
classification
and
91%
(Random
Forest
Regressor)
Regression.
Kmeans
clustering
model
provided
better
results
analysis
based
on
Silhouette,
Calinski-Harabasz,
Davies-Bouldin
metrics.
However,
there
was
certain
overfitting
prediction,
probably
due
to
limited
available
analysis.
addition,
interpolation
methods
might
have
affected
performance
by
producing
underfitting
results.
It
is
report
that
Gradient
classifier
mode
yielded
relatively
high
predicting
groundwater
quality
when
three
classes
used.
Random
Regressor
regression
returned
features
multiple
used
study.
This
work
reports
algorithms
can
predict
minimal
expense
expertise.
Toxics,
Journal Year:
2022,
Volume and Issue:
10(7), P. 342 - 342
Published: June 21, 2022
The
domestic
water
(DW)
quality
of
an
island
province
in
the
Philippines
that
experienced
two
major
mining
disasters
1990s
was
assessed
and
evaluated
2021
utilizing
heavy
metals
pollution
index
(MPI),
Nemerow's
(NPI),
total
carcinogenic
risk
(TCR)
index.
sources
its
DW
supply
from
groundwater
(GW),
surface
(SW),
tap
(TP),
refilling
stations
(WRS).
This
is
used
for
drinking
cooking
by
population.
In
situ
analyses
were
carried
out
using
Olympus
Vanta
X-ray
fluorescence
spectrometer
(XRF)
Accusensing
Metals
Analysis
System
(MAS)
G1
target
metalloids
(HMM)
arsenic
(As),
barium
(Ba),
copper
(Cu),
iron
(Fe),
lead
(Pb),
manganese
(Mn),
nickel
(Ni),
zinc
(Zn).
Monte
Carlo
(MC)
method
while
a
machine
learning
geostatistical
interpolation
(MLGI)
technique
employed
to
create
spatial
maps
metal
concentrations
health
indices.
MPI
values
calculated
at
all
sampling
locations
samples
indicated
high
pollution.
Additionally,
NPI
computed
categorized
as
"highly
polluted".
results
showed
quotient
indices
(HQI)
As
Pb
significantly
greater
than
1
sources,
indicating
probable
significant
(HR)
population
province.
exhibited
highest
(CR),
which
observed
TW
samples.
accounted
89.7%
CR
TW.
Furthermore,
exceeded
recommended
maximum
threshold
level
1.0
×
10-4
USEPA.
Spatial
distribution
contaminant
risks
provide
valuable
information
households
guide
local
government
units
well
regional
national
agencies
developing
strategic
interventions
improve
Toxics,
Journal Year:
2022,
Volume and Issue:
10(2), P. 95 - 95
Published: Feb. 18, 2022
Limited
monitoring
activities
to
assess
data
on
heavy
metal
(HM)
concentration
contribute
worldwide
concern
for
the
environmental
quality
and
degree
of
toxicants
in
areas
where
there
are
elevated
metals
concentrations.
Hence,
this
study
used
in-situ
physicochemical
parameters
limited
HM
SW
GW.
The
site
was
Marinduque
Island
Province
Philippines,
which
experienced
two
mining
disasters.
Prediction
model
results
showed
that
models
during
dry
wet
seasons
recorded
a
mean
squared
error
(MSE)
ranging
from
6
×
10
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(14), P. 2681 - 2681
Published: July 22, 2024
Salinization
is
a
major
soil
degradation
process
threatening
ecosystems
and
posing
great
challenge
to
sustainable
agriculture
food
security
worldwide.
This
study
aimed
evaluate
the
potential
of
state-of-the-art
machine
learning
algorithms
in
salinity
(EC1:5)
mapping.
Further,
we
predicted
distribution
patterns
under
different
future
scenarios
Yellow
River
Delta.
A
geodatabase
comprising
201
samples
19
conditioning
factors
(containing
data
based
on
remote
sensing
images
such
as
Landsat,
SPOT/VEGETATION
PROBA-V,
SRTMDEMUTM,
Sentinel-1,
Sentinel-2)
was
used
compare
predictive
performance
empirical
bayesian
kriging
regression,
random
forest,
CatBoost
models.
The
model
exhibited
highest
with
both
training
testing
datasets,
an
average
MAE
1.86,
RMSE
3.11,
R2
0.59
datasets.
Among
explanatory
factors,
Na
most
important
for
predicting
EC1:5,
followed
by
normalized
difference
vegetation
index
organic
carbon.
Soil
EC1:5
predictions
suggested
that
Delta
region
faces
severe
salinization,
particularly
coastal
zones.
three
increases
carbon
content
(1,
2,
3
g/kg),
2
g/kg
scenario
resulted
best
improvement
effect
saline–alkali
soils
>
ds/m.
Our
results
provide
valuable
insights
policymakers
improve
land
quality
plan
regional
agricultural
development.
