AIChE Journal,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 25, 2024
Abstract
CFD‐PBM
numerical
simulation
is
a
powerful
tool
in
the
research
of
droplet
swarm
behavior.
In
this
work,
an
artificial
neural
network
(ANN)
based
breakage
frequency
function
established
on
directly
measured
data
from
our
previous
studies.
Then,
weights
and
biases
ANN
are
embedded
into
code
form
matrices
vectors.
For
first
time,
CFD‐PBM‐ANN
framework
established.
Simulation
results
good
agreement
with
experimental
under
different
operation
conditions.
The
cumulative
size
distribution
decreases
increase
interfacial
tension
pulse
intensity.
It
also
found
by
that
relatively
high
at
edge
disc
doughnut
plate,
which
accordant
turbulent
energy
dissipation
velocity
gradient.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Aug. 28, 2024
This
study
presents
an
innovative
approach
for
predicting
water
and
groundwater
quality
indices
(WQI
GWQI)
in
the
Eastern
Province
of
Saudi
Arabia,
addressing
critical
challenges
scarcity
pollution
arid
regions.
Recent
literature
highlights
increasing
attention
towards
WQI
based
on
index
(WPI)
GWQI
as
essential
tools
simplifying
complex
hydrogeological
data,
thereby
facilitating
effective
management
protection.
Unlike
previous
works,
present
research
introduces
a
novel
hybrid
method
that
integrates
non-parametric
kernel
Gaussian
learning
(GPR),
adaptive
neuro-fuzzy
inference
system
(ANFIS),
decision
tree
(DT)
algorithms.
marks
first
application
prediction
offering
significant
advancement
field.
Through
laboratory
analysis
combination
various
machine
(ML)
techniques,
this
enhances
capabilities,
particularly
unmonitored
sites
semi-arid
The
study's
objectives
include
feature
engineering
dependency
sensitivity
to
identify
most
influential
variables
affecting
GWQI,
development
predictive
models
using
ANFIS,
GPR,
DT
both
indices.
Furthermore,
it
aims
assess
impact
different
data
portions
predictions,
exploring
divisions
such
(70%
/
30%),
(60%
40%),
(80%
20%)
training
testing
phase,
respectively.
By
filling
gap
resource
management,
offers
implications
regions
facing
similar
environmental
challenges.
its
methodology
comprehensive
analysis,
contributes
broader
effort
managing
protecting
resources
areas.
result
proved
GPR-M1
exhibited
exceptional
phase
accuracy
with
RMSE
=
0.0169
GWQI.
Similarly,
WPI,
ANFIS-M1
achieved
high
skills
0.0401.
results
emphasize
role
quantity
enhancing
model
robustness
precision
assessment.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
80, P. 102482 - 102482
Published: Jan. 21, 2024
Caenorhabditis
elegans
is
a
representative
organism
whose
DNA
structure
has
been
fully
elucidated.
It
used
as
model
for
various
analyses,
including
genetic
functional
analysis,
individual
behavioral
and
group
analysis.
Recently,
it
also
studied
an
important
bioindicator
of
water
pollution.
In
previous
studies,
traditional
machine
learning
methods,
such
the
Hidden
Markov
Model
(HMM),
were
to
determine
pollution
identify
pollutants
based
on
differences
in
swimming
behavior
C.
before
after
exposure
chemicals.
However,
these
models
have
low
accuracy
relatively
high
false-negative
rate.
This
study
proposes
method
detecting
identifying
types
using
Long
Short-Term
Memory
(LSTM)
model,
deep
suitable
time-series
data
The
activities
each
image
frames
are
characterized
by
Branch
Length
Similarity
(BLS)
entropy
profile.
These
BLS
profiles
converted
into
input
vectors
through
additional
preprocessing
two
clustering
methods.
We
conduct
experiments
formaldehyde
benzene
at
0.1
mg/L
each,
with
observation
time
intervals
varying
from
30
180
s.
performance
proposed
compared
that
previously
HMM
approach
variants
LSTM
models,
Gated
Recurrent
Unit
(GRU)
Bidirectional
(BiLSTM).
Water Research,
Journal Year:
2024,
Volume and Issue:
256, P. 121585 - 121585
Published: April 8, 2024
Artificial
intelligence
(AI)
is
expected
to
transform
many
scientific
disciplines,
with
the
potential
significantly
accelerate
discovery.
This
perspective
calls
for
development
of
data-centric
water
engineering
tackle
challenges
in
a
changing
world.
Building
on
historical
evolution
from
empirical
and
theoretical
paradigms
current
computational
paradigm,
we
argue
that
fourth
i.e.,
engineering,
emerging
driven
by
recent
AI
advances.
Here
define
new
framework
which
data
are
transformed
into
knowledge
insight
through
pipeline
powered
technologies.
It
proposed
embraces
three
principles
–
data-first,
integration
decision
making.
We
envision
needs
an
interdisciplinary
research
community,
shift
mindset
culture
academia
industry,
ethical
risk
guide
application
AI.
hope
this
paper
could
inspire
will
paradigm
towards
sector
fundamentally
planning
management
infrastructure.
Water Resources Research,
Journal Year:
2024,
Volume and Issue:
60(7)
Published: July 1, 2024
Abstract
Pressure
and
flow
estimation
in
water
distribution
networks
(WDNs)
allows
management
companies
to
optimize
their
control
operations.
For
many
years,
mathematical
simulation
tools
have
been
the
most
common
approach
reconstructing
an
estimate
of
WDNs
hydraulics.
However,
pure
physics‐based
simulations
involve
several
challenges,
for
example,
partially
observable
data,
high
uncertainty,
extensive
manual
calibration.
Thus,
data‐driven
approaches
gained
traction
overcome
such
limitations.
In
this
work,
we
combine
modeling
graph
neural
(GNN),
a
approach,
address
pressure
problem.
Our
work
has
two
main
contributions.
First,
training
strategy
that
relies
on
random
sensor
placement
making
our
GNN‐based
model
robust
unexpected
location
changes.
Second,
realistic
evaluation
protocol
considers
real
temporal
patterns
noise
injection
mimic
uncertainties
intrinsic
real‐world
scenarios.
As
result,
new
state‐of‐the‐art
model,
GAT
with
Res
idual
Connections,
is
available.
surpasses
performance
previous
studies
benchmarks,
showing
reduction
absolute
error
≈40%
average.
Water,
Journal Year:
2024,
Volume and Issue:
16(22), P. 3328 - 3328
Published: Nov. 19, 2024
Assessing
diverse
parameters
like
water
quality,
quantity,
and
occurrence
of
hydrological
extremes
their
management
is
crucial
to
perform
efficient
resource
(WRM).
A
successful
WRM
strategy
requires
a
three-pronged
approach:
monitoring
historical
data,
predicting
future
trends,
taking
controlling
measures
manage
risks
ensure
sustainability.
Artificial
intelligence
(AI)
techniques
leverage
these
knowledge
fields
single
theme.
This
review
article
focuses
on
the
potential
AI
in
two
specific
areas:
supply-side
demand-side
measures.
It
includes
investigation
applications
leak
detection
infrastructure
maintenance,
demand
forecasting
supply
optimization,
treatment
desalination,
quality
pollution
control,
parameter
calibration
optimization
applications,
flood
drought
predictions,
decision
support
systems.
Finally,
an
overview
selection
appropriate
suggested.
The
nature
adoption
investigated
using
Gartner
hype
cycle
curve
indicated
that
learning
application
has
advanced
different
stages
maturity,
big
data
reach
plateau
productivity.
also
delineates
pathways
expedite
integration
AI-driven
solutions
harness
transformative
capabilities
for
protection
global
resources.
