Hydrogeochemical evaluation of groundwater evolution and quality in some Voltaian aquifers of Kintampo South District, Bono East Region, Ghana: Implications from chemometric analysis, geochemical modeling and geospatial mapping techniques
HydroResearch,
Год журнала:
2024,
Номер
8, С. 13 - 27
Опубликована: Сен. 7, 2024
Язык: Английский
Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region
Loganathan Krishnamoorthy,
V. Lakshmanan
Environmental Science and Pollution Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 4, 2024
Язык: Английский
Machine Learning-based Model for Groundwater Quality Prediction: A Comprehensive Review and Future Time–Cost Effective Modelling Vision
Archives of Computational Methods in Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 19, 2025
Язык: Английский
Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis
Water,
Год журнала:
2025,
Номер
17(7), С. 936 - 936
Опубликована: Март 23, 2025
Groundwater,
which
constitutes
95%
of
the
world’s
freshwater
resources,
is
widely
used
for
drinking
and
domestic
water
supply,
agricultural
irrigation,
energy
production,
bottled
commercial
use.
In
recent
years,
due
to
pressures
from
climate
change
excessive
urbanization,
a
noticeable
decline
in
groundwater
levels
has
been
observed,
particularly
arid
semi-arid
regions.
The
corresponding
changes
have
analyzed
using
diverse
range
methodologies,
including
data-driven
modeling
techniques.
Recent
evidence
shown
notable
acceleration
utilization
such
advanced
techniques,
demonstrating
significant
attention
by
research
community.
Therefore,
major
aim
present
study
conduct
bibliometric
analysis
investigate
application
evolution
machine
learning
(ML)
techniques
research.
this
sense,
studies
published
between
2000
2023
were
examined
terms
scientific
productivity,
collaboration
networks,
themes,
methods.
findings
revealed
that
ML
offer
high
accuracy
predictive
capacity,
especially
quality,
level
estimation,
pollution
modeling.
United
States,
China,
Iran
stand
out
as
leading
countries
emphasizing
strategic
importance
management.
However,
outcomes
demonstrated
low
international
cooperation
led
deficiencies
solving
transboundary
problems.
aimed
encourage
more
widespread
effective
use
management
environmental
planning
processes
drew
transparent
interpretable
algorithms,
with
potential
yield
rewarding
opportunities
increasing
adoption
technologies
decision-makers.
Язык: Английский
Irrigation Water Quality Prognostication: An Innovative Ensemble Architecture Leveraging Deep Learning and Machine Learning for Enhanced SAR and ESP Estimation in the East Coast of India
Journal of environmental chemical engineering,
Год журнала:
2025,
Номер
unknown, С. 116433 - 116433
Опубликована: Апрель 1, 2025
Язык: Английский
Assessment of Climate Change Impacts on Hydrology Using an Integrated Water Quality Index
Sangung Lee,
Bu Geon Jo,
Jaeyeon Lim
и другие.
Hydrology,
Год журнала:
2024,
Номер
11(11), С. 178 - 178
Опубликована: Окт. 24, 2024
Traditional
Water
Quality
Indices
(WQIs)
often
fail
to
capture
the
significant
impact
of
flow
velocity
on
water
quality,
especially
under
varying
hydrological
conditions.
In
this
study,
an
Integrated
Index
(IWQI)
was
developed
by
combining
quality
parameters
and
rate,
providing
a
more
comprehensive
assessment
various
Compared
traditional
indices,
IWQI
showed
slightly
lower
correlations
in
individual
parameter
performance,
but
it
performed
well
evaluating
changes
associated
with
variations.
Parameters
such
as
Total
Phosphorus
(TP),
Coliforms
(TC),
Fecal
(FC),
which
are
prevalent
pollutants
Cheongmi
River,
significantly
influenced
scores.
River
evaluated
using
input
data
simulated
climate
change
scenario.
When
precipitation
abundant,
score
remained
relatively
stable
even
reduced
rates.
However,
during
periods
insufficient
rainfall,
deteriorated
sharply.
While
general
exhibited
approximately
10%
decreased,
TC
FC
rapid
deterioration,
rates
ranging
from
20%
60%.
These
findings
underscore
importance
managing
FC,
particularly
when
rainfall
is
predicted,
they
major
sources
pollution.
Язык: Английский
Using Random Forest Regression to Model the Spatial Distribution of Concentrations of Selected Metals in Groundwater in Forested Areas of the Wielkopolska National Park, Poland
Forests,
Год журнала:
2024,
Номер
15(12), С. 2191 - 2191
Опубликована: Дек. 12, 2024
Monitoring
groundwater
pollution
is
an
important
issue
in
terms
of
analyzing
threats
to
protected,
environmentally
valuable
areas.
The
topographical
and
environmental
characteristics
a
given
area
are
often
mentioned
among
the
factors
affecting
dynamics
chemistry
groundwater.
In
this
study,
random
forest
regression
(RFR)
model
was
used
determine
spatial
distribution
selected
metals,
such
as
aluminum,
calcium,
iron,
potassium,
magnesium,
manganese,
sodium,
zinc.
role
indicators
describing
terrain
variability,
derivatives
digital
elevation
(DEM)
were
employed,
with
resolution
5
m,
topography
on
local
scale,
as,
others,
slopes,
aspect
curvatures
topographic
position
index,
SAGA
wetness
well
generalized
values
determined
for
each
sampling
point
areas
contributing
their
runoff.
addition,
parameters
taken
into
consideration:
habitat
types,
structure
soil
cover,
seasons
when
samples
collected.
This
study
collected
from
15
wells
located
forested
Wielkopolska
National
Park
seven
dates.
results
obtained
show
that
can
be
very
good
variability
concentrations
sodium
However,
case
calcium
zinc,
no
correlations
found
between
adopted
degree
importance
predictor
order
rank
modeling
concentration
metals
summary
ranking
predictors
indicates
strongest
influence
predicted
exhibited
by
profile
curvatures,
planar
multiscale
TPI,
then
type
forest.
On
other
hand,
curvature
classifications,
composition,
seasonality
exhibit
smallest
impact
modeling.
Язык: Английский