Integrative Assessment of Surface Water Contamination Using GIS, WQI, and Machine Learning in Urban–Industrial Confluence Zones Surrounding the National Capital Territory of the Republic of India
Water,
Journal Year:
2025,
Volume and Issue:
17(7), P. 1076 - 1076
Published: April 4, 2025
This
study
proposes
an
innovative
framework
integrating
geographic
information
systems
(GISs),
water
quality
index
(WQI)
analysis,
and
advanced
machine
learning
(ML)
models
to
evaluate
the
prevalence
impact
of
organic
inorganic
pollutants
across
urban–industrial
confluence
zones
(UICZ)
surrounding
National
Capital
Territory
(NCT)
India.
Surface
samples
(n
=
118)
were
systematically
collected
from
Gautam
Buddha
Nagar,
Ghaziabad,
Faridabad,
Sonipat,
Gurugram,
Jhajjar,
Baghpat
districts
assess
physical,
chemical,
microbiological
parameters.
The
application
spatial
interpolation
techniques,
such
as
kriging
inverse
distance
weighting
(IDW),
enhances
WQI
estimation
in
unmonitored
areas,
improving
regional
assessments
remediation
planning.
GIS
mapping
highlighted
stark
disparities,
with
industrial
hubs,
like
Faridabad
exhibiting
values
exceeding
600
due
untreated
discharges
wastewater,
while
rural
regions,
Jhajjar
Baghpat,
recorded
below
200,
reflecting
minimal
anthropogenic
pressures.
employed
four
ML
models—linear
regression
(LR),
random
forest
(RF),
Gaussian
process
(GPR_PUK),
support
vector
machines
(SVM_Poly)—to
predict
high
precision.
SVM_Poly
emerged
most
effective
model,
achieving
testing
CC,
RMSE,
MAE
0.9997,
11.4158,
5.6085,
respectively,
outperforming
RF
(0.9925,
29.8107,
21.7398)
GPR_PUK
(0.9811,
68.4466,
54.0376).
By
leveraging
models,
this
prediction
beyond
conventional
computation,
enabling
extrapolation
early
contamination
detection
data-scarce
regions.
Sensitivity
analysis
identified
total
suspended
solids
critical
predictor
influencing
WQI,
underscoring
its
relevance
monitoring
programs.
research
uniquely
integrates
algorithms
analytics,
providing
a
novel
methodological
contribution
assessment.
findings
emphasize
urgency
mitigating
fate
transport
protect
Delhi’s
hydrological
ecosystems,
presenting
robust
decision-support
system
for
policymakers
environmental
managers.
Language: Английский
Groundwater quality assessment for irrigation in coastal region (Güzelyurt), Northern Cyprus and importance of empirical model for predicting groundwater quality (electric conductivity)
Environmental Earth Sciences,
Journal Year:
2025,
Volume and Issue:
84(8)
Published: April 1, 2025
Language: Английский
Evaluation of the performance and complexity of water quality models for peatlands
Emmanuel Opoku-Agyemang,
No information about this author
Mark G. Healy,
No information about this author
Mingming Tong
No information about this author
et al.
Journal of Hydrology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 132421 - 132421
Published: Nov. 1, 2024
Language: Английский
Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models
Journal of Cleaner Production,
Journal Year:
2024,
Volume and Issue:
unknown, P. 144300 - 144300
Published: Nov. 1, 2024
Language: Английский