Water
quality
is
crucial
as
it
directly
affects
the
ecosystem
and
human
health.
However,
current
water
classification
methods
are
inefficient
because
they
do
not
compare
prediction
accuracy
between
machine
learning
methods.
In
this
regard,
objective
of
study
to
classify
based
on
proposed
tools.
To
fulfill
that,
a
preliminary
was
conducted
by
collecting
related
information
in
research
domain
through
articles,
electronic
books,
online
databases.
The
data
collection
for
prototype's
dataset
obtained
from
an
book
published
Pakistan
Council
Research
Resources
2021.
Subsequently,
pre-processing
phase
using
WEKA
software
which
includes
steps
transform
into
cleaner
format
make
model
more
accurate.
each
technique
developed
Python
Jupyter
Notebook.
results
score
were
also
phase.
findings
show
that
Decision
Tree
performs
excellently
with
97.37%
compared
Support
Vector
Machine
K-Nearest
Neighbour
models,
95.69%
74.72%,
respectively.
Consequently,
implementing
multi-class
system
can
help
future
researchers
accurately
reduce
misclassification
quality.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(17), P. 7809 - 7809
Published: Sept. 3, 2024
A
digital
twin
is
a
new
trend
in
the
development
of
current
smart
water
conservancy
industry.
The
main
research
content
intelligent
clarified.
This
paper
first
summarizes
and
combs
relevant
system
architecture
conservancy,
puts
forward
framework
based
on
twins,
highlighting
characteristics
virtual
real
interaction,
symbiosis
platform.
Secondly,
status
quo
“sky,
air,
ground
water”
integrated
monitoring
technology,
big
data
artificial
intelligence,
model
platform
knowledge
graph
security
technology
analyzed.
From
perspective
application,
progress
each
security,
resources
hydraulic
engineering
reviewed.
Although
construction
has
made
remarkable
progress,
it
still
faces
many
challenges
such
as
governance,
integration
innovation,
standardization.
In
view
these
challenges,
this
series
countermeasures,
looks
to
future
direction
conservancy.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
154, P. 110907 - 110907
Published: Sept. 7, 2023
The
existence
of
cascade
reservoirs
in
complex
ecosystems
not
only
assists
humans
to
regulate
and
use
water
resources
efficiently,
but
the
presence
hydropower
also
contributes
reducing
carbon
emissions.
In
recent
decades,
ecology
sources
comprising
has
been
subject
a
wide
range
threats.
Assessing
these
threats
is
essential
for
sustainable
operation
management
strategies
reservoirs.
This
paper
aimed
propose
an
ecological
security
assessment
framework
using
improved
Driver-Pressure-State-Impact-Response
model
assess
typical
riverine
reservoir
located
subtropical
region
Zhejiang,
China.
Combining
quantitative
qualitative
methods
describe
indicators
involved
social,
economic,
ecological,
management.
Simultaneously,
degree
deviation
from
standard
state
study
area
assessed
with
help
index.
analysis
shows
that
index
four
years
increased
81.67
2019
90.14,
where
lowest
value
socio-economic
impact
index,
indicating
score
higher
than
90.00,
activities
on
more
pronounced
comparison
other
aspects.
control
92.70
2022
plays
vital
connecting
role.
Good
increases
growth
by
16.82%,
improves
health
10.23%,
enhances
ecosystem
services
5.25%
compared
2019.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(21), P. 10052 - 10052
Published: Nov. 4, 2024
This
research
developed
smart
integrated
hybrid
renewable
systems
for
small
energy
communities
and
applied
them
to
a
real
system
achieve
self-sufficiency
promote
sustainable
decentralized
generation.
It
compares
stand-alone
(SA)
grid-connected
(GC)
configurations
using
optimized
mathematical
model
data-driven
optimization,
with
economic
analysis
of
various
combinations
(PV,
Wind,
PHS,
BESS,
Grid)
search
the
optimal
solution.
Four
cases
were
developed:
two
(SA1:
PV
+
Wind
SA2:
PHS
BESS)
(GC1:
Grid,
GC2:
Grid).
GC2
shows
most
economical
stable
cash
flow
(−€123.2
annually),
low
CO2
costs
(€367.2),
91.7%
grid
independence,
requiring
125
kW
installed
power.
While
GC
options
had
lower
initial
investments
(between
€157k
€205k),
SA
provided
levelized
(LCOE)
ranging
from
€0.039
€0.044/kWh.
The
integration
pumped
hydropower
storage
enhances
supporting
peak
loads
up
days
capacity
2.17
MWh.
Engineering Applications of Computational Fluid Mechanics,
Journal Year:
2024,
Volume and Issue:
18(1)
Published: Nov. 6, 2024
In
recent
decades,
securing
drinkable
water
sources
has
become
a
pressing
concern
for
populations
in
various
regions
worldwide.
Therefore,
to
address
the
growing
need
potable
water,
contemporary
purification
technologies
can
be
employed
convert
saline
into
supplies.
prediction
of
important
parameters
desalination
plants
is
key
task
designing
and
implementing
these
facilities.
this
regard,
artificial
intelligence
techniques
have
proven
powerful
assets
field.
These
methods
offer
an
expedited
effective
means
estimating
parameters,
thus
catalyzing
their
implementation
real-world
scenarios.
study,
predictive
accuracy
six
different
machine
learning
models,
including
Natural
Gradient-based
Boosting
(NGBoost),
Adaptive
(AdaBoost),
Categorical
(CatBoost),
Support
vector
regression
(SVR),
Gaussian
Process
Regression
(GPR),
Extremely
Randomized
Tree
(ERT)
was
evaluated
modelling
parameter
permeate
flow
as
element
system
efficiency,
energy
consumption,
quality
using
input
combinations
feed
salt
concentration,
condenser
inlet
temperature,
rate,
evaporator
temperature.
The
next
phase
research
SHAP
interpretability
method
illustrate
impact
individual
variables
on
model's
output.
Moreover,
performance
developed
frameworks
set
five
dependable
statistical
measures:
RMSE,
NS,
MAE,
MAPE
R2.
indicators
were
utilized
provide
robust
gauging
precision
forecasts.
A
comparative
analysis
outcomes,
measured
by
RMSE
criteria,
revealed
that
SVR
technique
(RMSE
=
0.125
L/(h·m2))
exhibited
superior
compared
NGBoost
0.163
L/(h·m2)),
AdaBoost
0.219
CatBoost
0.149
GPR
0.156
ERT
0.167
methodologies
predicting
rates.
outcomes
obtained
during
evaluation
stage
demonstrated
efficacy
algorithm
enhancing
forecasts,
utilizing
relevant
variables.
Water
quality
is
crucial
as
it
directly
affects
the
ecosystem
and
human
health.
However,
current
water
classification
methods
are
inefficient
because
they
do
not
compare
prediction
accuracy
between
machine
learning
methods.
In
this
regard,
objective
of
study
to
classify
based
on
proposed
tools.
To
fulfill
that,
a
preliminary
was
conducted
by
collecting
related
information
in
research
domain
through
articles,
electronic
books,
online
databases.
The
data
collection
for
prototype's
dataset
obtained
from
an
book
published
Pakistan
Council
Research
Resources
2021.
Subsequently,
pre-processing
phase
using
WEKA
software
which
includes
steps
transform
into
cleaner
format
make
model
more
accurate.
each
technique
developed
Python
Jupyter
Notebook.
results
score
were
also
phase.
findings
show
that
Decision
Tree
performs
excellently
with
97.37%
compared
Support
Vector
Machine
K-Nearest
Neighbour
models,
95.69%
74.72%,
respectively.
Consequently,
implementing
multi-class
system
can
help
future
researchers
accurately
reduce
misclassification
quality.
