Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 241 - 284
Опубликована: Дек. 6, 2024
This
chapter
aims
to
explore
the
transformative
potential
of
artificial
intelligence
(AI),
machine
learning
(ML)
and
digital
innovations
in
water
resource
management
monitoring.
It
discusses
various
AI
techniques
tools
that
enhance
controlling,
analysis
managing
resources.
These
are
designed
address
challenges
such
as
data
quality,
technologies
integration,
real-time
decision-making.
There
several
case
studies
chapter,
demonstrating
successful
implementation
ML
demand
prediction,
quality
monitoring,
optimizing
irrigation,
efficient
utilization
detecting
anomalies
systems.
The
emphasizes
need
for
interdisciplinary
collaboration,
robust
governance,
ethical
considerations
fully
realize
benefits
sustainable
management.
Water,
Год журнала:
2025,
Номер
17(7), С. 923 - 923
Опубликована: Март 22, 2025
The
Zhuxihe
River
has
faced
significant
water
quality
challenges
in
recent
years.
Although
control
measures
have
been
implemented,
the
pollution
levels
remain
concerning.
This
paper
aims
to
investigate
spatio-temporal
variations
of
through
field
sampling,
chemical
testing,
and
synthetic
evaluation.
We
collected
52
samples
both
dry
wet
seasons
along
main
river
its
tributaries.
evaluation,
which
utilized
integrated
SFE-FCE
method,
identified
MnO42−,
NH3-N,
TP,
TFe
as
primary
pollutants.
In
season,
MnO42−
concentrations
ranged
from
1.6
mg/L
19.8
mg/L,
NH3-N
0.12
2.04
TP
varied
0.1
5.61
mg/L.
4.9
13.9
0.27
1.73
0.07
1.31
results
indicate
are
higher
show
seasonal
fluctuations.
Spatially,
downstream
section
faces
highest
levels.
study
provides
insights
into
dynamics
River,
offering
a
scientific
foundation
for
future
research
management
strategies.
