Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
146, P. 109845 - 109845
Published: Jan. 2, 2023
Dissolved
oxygen
(DO)
is
an
essential
indicator
for
assessing
water
quality
and
managing
aquatic
environments,
but
it
still
a
challenging
topic
to
accurately
understand
predict
the
spatiotemporal
variation
of
DO
concentrations
under
complex
effects
different
environmental
factors.
In
this
study,
practical
prediction
framework
was
proposed
based
on
support
vector
regression
(SVR)
model
coupling
multiple
intelligence
techniques
(i.e.,
four
data
denoising
techniques,
three
feature
selection
rules,
hyperparameter
optimization
methods).
The
holistic
tested
using
matrix
(17,532
observation
in
total)
12
indicators
from
vital
monitoring
stations
longest
inter-basin
diversion
project
world
Middle-Route
South-to-North
Water
Diversion
Project
China),
during
year
2017
2020
period.
results
showed
that
we
advocated
could
successfully
concentration
variations
geographical
locations.
used
"wavelet
analysis–LASSO
regression–random
search–SVR"
combination
Waihuanhe
station
has
best
performance,
with
Root
Mean
Square
Error
(RMSE),
(MSE),
Absolute
(MAE),
coefficient
determination
(R2)
values
0.251,
0.063,
0.190,
0.911,
respectively.
combined
methods
can
significantly
promote
robustness
accuracy
provide
new
universal
way
investigating
understanding
drivers
variations.
For
management
department,
comprehensive
also
identify
reveal
key
parameters
should
be
concerned
monitored
factors
change.
More
studies
terms
potential
integrated
risk
multi-indicators
mega
projects
and/or
similar
bodies
are
required
future.
Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(22), P. 12147 - 12147
Published: Nov. 8, 2023
This
paper
offers
a
comprehensive
overview
of
machine
learning
(ML)
methodologies
and
algorithms,
highlighting
their
practical
applications
in
the
critical
domain
water
resource
management.
Environmental
issues,
such
as
climate
change
ecosystem
destruction,
pose
significant
threats
to
humanity
planet.
Addressing
these
challenges
necessitates
sustainable
management
increased
efficiency.
Artificial
intelligence
(AI)
ML
technologies
present
promising
solutions
this
regard.
By
harnessing
AI
ML,
we
can
collect
analyze
vast
amounts
data
from
diverse
sources,
remote
sensing,
smart
sensors,
social
media.
enables
real-time
monitoring
decision
making
applications,
including
irrigation
optimization,
quality
monitoring,
flood
forecasting,
demand
enhance
agricultural
practices,
distribution
models,
desalination
plants.
Furthermore,
facilitates
integration,
supports
decision-making
processes,
enhances
overall
sustainability.
However,
wider
adoption
faces
challenges,
heterogeneity,
stakeholder
education,
high
costs.
To
provide
an
management,
research
focuses
on
core
fundamentals,
major
(prediction,
clustering,
reinforcement
learning),
ongoing
issues
offer
new
insights.
More
specifically,
after
in-depth
illustration
algorithmic
taxonomy,
comparative
mapping
all
specific
tasks.
At
same
time,
include
tabulation
works
along
with
some
concrete,
yet
compact,
descriptions
objectives
at
hand.
leveraging
tools,
develop
plans
address
world’s
supply
concerns
effectively.
Water,
Journal Year:
2024,
Volume and Issue:
16(3), P. 472 - 472
Published: Jan. 31, 2024
Water
resource
modeling
is
an
important
means
of
studying
the
distribution,
change,
utilization,
and
management
water
resources.
By
establishing
various
models,
resources
can
be
quantitatively
described
predicted,
providing
a
scientific
basis
for
management,
protection,
planning.
Traditional
hydrological
observation
methods,
often
reliant
on
experience
statistical
are
time-consuming
labor-intensive,
frequently
resulting
in
predictions
limited
accuracy.
However,
machine
learning
technologies
enhance
efficiency
sustainability
by
analyzing
extensive
hydrogeological
data,
thereby
improving
optimizing
utilization
allocation.
This
review
investigates
application
predicting
aspects,
including
precipitation,
flood,
runoff,
soil
moisture,
evapotranspiration,
groundwater
level,
quality.
It
provides
detailed
summary
algorithms,
examines
their
technical
strengths
weaknesses,
discusses
potential
applications
modeling.
Finally,
this
paper
anticipates
future
development
trends
to
