Accurate
pathogenic
detection
in
wastewater
is
critical
for
safeguarding
public
health
and
the
environment.
However,
presence
of
free
nucleic
acids
samples
poses
significant
challenges
to
molecular
accuracy.
This
comprehensive
review
explores
current
status
future
potential
pretreatment
methods
remove
from
samples.
The
study
contributes
a
analysis
mechanisms,
strengths,
limitations
various
approaches,
including
physical,
chemical,
enzymatic
processes.
effect
factors
on
removal
efficiency
these
also
discussed.
enhances
our
comprehension
techniques
their
vital
role
achieving
precise
complex
matrices.
Furthermore,
it
outlines
perspectives
developments
improving
speed
effectiveness
detection,
contributing
significantly
disease
surveillance,
early
warning
systems,
environmental
protection.
Water,
Journal Year:
2025,
Volume and Issue:
17(2), P. 160 - 160
Published: Jan. 9, 2025
Understanding
influential
factors
for
fecal
contamination
in
groundwater
is
critical
ensuring
water
safety
and
public
health.
The
objective
of
this
study
to
identify
key
shallow
tubewells
using
machine
learning
methods.
Three
methods,
including
recursive
feature
elimination
(RFE)
with
XGBoost,
Random
Forest,
mutual
information,
were
implemented
examine
E.
coli
presence
concentration
1495
tubewell
samples
Matlab,
Bangladesh.
For
presence,
climatic
variables,
average
rainfall
temperature
over
the
30,
15,
7
days
preceding
sampling,
as
well
ambient
on
sampling
day,
emerged
predictors.
Land
cover
characteristics,
such
percentages
urban
agricultural
areas
within
100
m
a
tubewell,
also
significant.
concentration,
land
characteristics
m,
number
hot
heavy-rain
30
3
day
identified
drivers.
Forest
information
yielded
results
that
more
similar
each
other
than
those
RFE
XGBoost.
findings
highlight
interplay
between
factors,
use,
population
density
determining
demonstrate
power
algorithms
ranking
these
factors.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
81, P. 102647 - 102647
Published: May 18, 2024
The
application
of
machine
learning
approaches
to
improve
groundwater
salinity
risk
mapping
is
limited
despite
all
potential
advantages.
Therefore,
there
an
ongoing
need
for
investigations
that
present
new
techniques,
like
learning,
validated
against
conventional
methods.
These
advances
are
particularly
important
arid
and
semiarid
regions,
such
as
the
Bakhtegan
basin
(southern
Iran),
where
use
may
far
outweigh
recharge,
but
management
improvements
essential.
To
address
these
needs,
hazard,
vulnerability,
maps
were
investigated
using
integrated
statistical
(i.e.,
frequency
ratio
index
models),
Random
Forest
Classification
Regression
Trees
algorithms),
decision-making
models
(fuzzy
analytic
hierarchy
process
(FAHP)).
Results
showed
was
high
in
central
areas
region
well
at
margins
Lake
irrigated
farming
lands.
Based
on
modeling
results
testing
phase,
it
found
Frequency
Ratio
exhibited
better
performance,
with
Nash–Sutcliffe
efficiency
metrics
0.73
0.70,
respectively,
compared
Statistical
Index
models,
which
had
0.65
0.63,
respectively.
innovative
techniques
developed
current
work
accurately
identified
variables
normalized
difference
index,
distance
from
mines,
land
associated
highest
weight
values
(0.59,
0.14,
0.11,
respectively)
other
identify
vulnerability.
fuzzy
method
developing
vulnerability
maps,
integrating
them
hazard
this
study.
approach
transferrable
susceptibility,
assessments
globally
study
area
similar
settings.
