Water,
Год журнала:
2024,
Номер
16(22), С. 3212 - 3212
Опубликована: Ноя. 8, 2024
The
prediction
of
the
chemical
oxygen
demand
(COD)
and
total
nitrogen
(TN)
in
integrated
anaerobic–anoxic–oxic
(A2O)
anoxic–oxic
(AO)
processes
(i.e.,
A2O+AO
process)
was
achieved
using
a
dynamic
ensemble
model
that
reflects
dynamics
wastewater
treatment
plants
(WWTPs).
This
effectively
captures
variability
influent
characteristics
fluctuations
within
each
reactor
process.
By
employing
time-lag
approach
based
on
hydraulic
retention
time
(HRT),
artificial
intelligence
(AI)
selects
suitable
input
pH,
temperature,
dissolved
solid
(TDS),
NH3-N,
NO3-N)
output
(COD
TN)
data
pairs
for
training,
minimizing
error
between
predicted
observed
values.
Data
collected
over
two
years
from
actual
process
were
utilized.
adopted
machine
learning-based
XGBoost
COD
TN
predictions.
outperformed
static
model,
with
mean
absolute
percentage
(MAPE)
ranging
9.5%
to
15.2%,
compared
model’s
range
11.4%
16.9%.
For
TN,
errors
ranged
9.4%
15.5%,
while
showed
lower
specific
reactors,
particularly
anoxic
oxic
stages
due
their
stable
characteristics.
These
results
indicate
is
predicting
water
quality
WWTPs,
especially
as
may
increase
external
environmental
factors
future.
World Journal of Advanced Research and Reviews,
Год журнала:
2024,
Номер
21(1), С. 1373 - 1382
Опубликована: Янв. 19, 2024
Integrating
the
Internet
of
Things
(IoT)
and
Artificial
Intelligence
(AI)
in
smart
water
management
revolutionizes
sustainable
resource
utilization.
This
comprehensive
review
explores
these
technologies'
benefits,
challenges,
regulatory
implications,
future
trends.
Smart
enhances
operational
efficiency,
predictive
maintenance,
conservation
while
addressing
data
security
infrastructure
investment
challenges.
Regulatory
frameworks
play
a
pivotal
role
shaping
responsible
deployment
AI
IoT,
ensuring
privacy
ethical
use.
Future
trends
include
advanced
sensors,
decentralized
systems,
quantum
computing,
blockchain
for
enhanced
security.
The
alignment
with
Sustainable
Development
Goals
(SDGs)
underscores
transformative
potential
achieving
universal
access
to
clean
water,
climate
resilience,
inclusive,
development.
As
we
embrace
technologies,
collaboration,
public
awareness,
considerations
will
guide
evolution
intelligent
equitable
systems.
Practice, progress, and proficiency in sustainability,
Год журнала:
2024,
Номер
unknown, С. 222 - 244
Опубликована: Июнь 28, 2024
Human-machine
interaction
plays
a
pivotal
role
in
realizing
energy-efficient
and
sustainable
urban
mobility.
There
is
vital
contribution
of
HMI
facilitating
more
environmentally
responsible
transportation
solutions.
Through
the
seamless
between
users,
smart
infrastructure,
autonomous
vehicles,
HMI-driven
approaches
promise
to
optimize
traffic
flows,
reduce
energy
consumption,
minimize
emissions.
In
rapidly
urbanizing
world,
evolution
smart-sustainable
mobility
pressing
concern,
necessitating
judicious
integration
cutting-edge
technology
with
ecological
sustainability.
This
chapter
explores
multifaceted
nexus
human-machine
interaction,
technology,
sustainability,
mobility,
specific
focus
on
footprint
within
context
systems.
Water,
Год журнала:
2024,
Номер
16(23), С. 3380 - 3380
Опубликована: Ноя. 24, 2024
This
study
presents
an
innovative
approach
utilizing
artificial
intelligence
(AI)
for
the
prediction
and
classification
of
water
quality
parameters
based
on
physico-chemical
measurements.
The
primary
objective
was
to
enhance
accuracy,
speed,
accessibility
monitoring.
Data
collected
from
various
samples
in
Algeria
were
analyzed
determine
key
such
as
conductivity,
turbidity,
pH,
total
dissolved
solids
(TDS).
These
measurements
integrated
into
deep
neural
networks
(DNNs)
predict
indices
sodium
adsorption
ratio
(SAR),
magnesium
hazard
(MH),
percentage
(SP),
Kelley’s
(KR),
potential
salinity
(PS),
exchangeable
(ESP),
well
Water
Quality
Index
(WQI)
Irrigation
(IWQI).
DNNs
model,
optimized
through
selection
activation
functions
hidden
layers,
demonstrated
high
precision,
with
a
correlation
coefficient
(R)
0.9994
low
root
mean
square
error
(RMSE)
0.0020.
AI-driven
methodology
significantly
reduces
reliance
traditional
laboratory
analyses,
offering
real-time
assessments
that
are
adaptable
local
conditions
environmentally
sustainable.
provides
practical
solution
resource
managers,
particularly
resource-limited
regions,
efficiently
monitor
make
informed
decisions
public
health
agricultural
applications.
Journal of Machine and Computing,
Год журнала:
2025,
Номер
unknown, С. 483 - 495
Опубликована: Янв. 3, 2025
The
rapid
proliferation
of
Internet
Things
(IoT)
networks
has
significantly
advanced
various
sectors
such
as
smart
cities,
healthcare,
and
industrial
automation,
but
it
also
introduced
substantial
security
challenges.
Protecting
data
integrity,
confidentiality,
availability
in
these
is
critical,
yet
traditional
measures
often
fall
short
due
to
the
decentralized
resource-constrained
nature
IoT
devices.
Low-Energy
Adaptive
Clustering
Hierarchy
(LEACH)
protocol,
designed
optimize
energy
consumption
sensor
networks,
lacks
intrinsic
features.
To
address
challenges,
this
paper
proposes
a
novel
approach
that
integrates
LEACH
with
Distributed
Ledger
Technology
(DLT),
specifically
blockchain.
Blockchain’s
immutable
ledger
can
enhance
integrity
within
networks.
methodology
involves
modifying
incorporate
blockchain
for
secure
transmission.
In
clustering
phase,
forms
clusters
designates
cluster
head
(CH)
aggregation
Each
CH
maintains
local
log
verify
transactions
its
cluster,
using
consensus
mechanism
ensure
integrity.
Smart
contracts
are
implemented
automate
policies
detect
anomalies,
while
encryption
digital
signatures
provide
additional
layers.
Simulations
NS-3
simulator
showed
promising
results:
was
reduced
by
18%
compared
LEACH,
latency
increased
5%
processing
overhead,
throughput
improved
12%,
metrics
indicated
25%
improvement
30%
reduction
successful
attack
attempts.
conclusion,
integrating
algorithm
enhances
efficiency
This
leverages
optimization
robust
framework
blockchain,
offering
scalable
solution
diverse
applications.
Future
research
will
focus
on
optimizing
operations
reduce
further
exploring
model's
applicability
scenarios.
Advanced Engineering Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 15, 2025
Artificial
intelligence
(AI)
and
machine
learning
(ML)
have
been
the
subjects
of
increased
interest
in
recent
years
due
to
their
benefits
across
several
fields.
One
sector
that
can
benefit
from
these
tools
is
tribology
industry,
with
an
emphasis
on
friction
wear
prediction.
This
industry
hopes
train
utilize
AI
algorithms
classify
equipment
life
status
forecast
component
failure,
mainly
using
supervised
unsupervised
approaches.
article
examines
some
methods
used
accomplish
this,
such
as
condition
monitoring
for
predictions
material
selection,
lubrication
performance,
lubricant
formulation.
Furthermore,
ML
support
determination
tribological
characteristics
engineering
systems,
allowing
a
better
fundamental
understanding
friction,
wear,
mechanisms.
Moreover,
study
also
finds
continued
use
requires
access
findable,
accessible,
interoperable,
reusable
data
ensure
integrity
prediction
tools.
The
advances
show
considerable
promise,
providing
more
accurate
extensible
than
traditional
E3S Web of Conferences,
Год журнала:
2025,
Номер
605, С. 03006 - 03006
Опубликована: Янв. 1, 2025
There
are
some
challenges
firms
the
wastewater
treatment,
numerous
hurdles
concerning
enhancement
of
energy
efficiency,
compliance
with
increasingly
stringent
water
quality
regulations,
and
maximizing
resource
recovery
opportunities.
In
recent
years,
computational
models
have
garnered
acknowledgment
as
potent
instruments
for
tackling
these
various
challenges,
bolstering
operational
economic
effectiveness
treatment
plants
(“WWTPs”).
Also,
review
discusses
application
(AI)
algorithms
on
(WWTPs),
predicting
(“WWTP”)
effluent
properties,
inflows,
anomaly
detecting,
optimization.
The
critical
gaps
future
directions
in
including
explain
ability
data-driven
or
transfer
Learning
processes
reinforcement
learning,
also
addressed.
Electronics,
Год журнала:
2025,
Номер
14(4), С. 696 - 696
Опубликована: Фев. 11, 2025
The
integration
of
artificial
intelligence
(AI)
agents
with
the
Internet
Things
(IoT)
has
marked
a
transformative
shift
in
environmental
monitoring
and
management,
enabling
advanced
data
gathering,
in-depth
analysis,
more
effective
decision
making.
This
comprehensive
literature
review
explores
AI
IoT
technologies
within
sciences,
particular
focus
on
applications
related
to
water
quality
climate
data.
methodology
involves
systematic
search
selection
relevant
studies,
followed
by
thematic,
meta-,
comparative
analyses
synthesize
current
research
trends,
benefits,
challenges,
gaps.
highlights
how
enhances
IoT’s
collection
capabilities
through
predictive
modeling,
real-time
analytics,
automated
making,
thereby
improving
accuracy,
timeliness,
efficiency
systems.
Key
benefits
identified
include
enhanced
precision,
cost
efficiency,
scalability,
facilitation
proactive
management.
Nevertheless,
this
encounters
substantial
obstacles,
including
issues
quality,
interoperability,
security,
technical
constraints,
ethical
concerns.
Future
developments
point
toward
enhancements
technologies,
incorporation
innovations
like
blockchain
edge
computing,
potential
formation
global
systems,
greater
public
involvement
citizen
science
initiatives.
Overcoming
these
challenges
embracing
new
technological
trends
could
enable
play
pivotal
role
strengthening
sustainability
resilience.
