Water Practice & Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 22, 2024
ABSTRACT
Semiarid
regions
are
facing
overexploitation
of
groundwater
resources
to
meet
irrigation
needs.
Monitoring
the
water-energy
nexus
allows
for
optimal
management
extracted
water
volumes
and
consumed
energy.
The
Nabeul
region
Tunisia
was
selected
where
14
farmers,
whose
wells
were
equipped
with
smart
electricity
meters
(SWEMs),
instant
monitoring
pumped
electrical
energy
required
irrigation.
Monthly
data
over
a
period
eight
months
used
study
variations
in
active
analysis
variance
classified
farmers
into
four
groups
based
on
five
Spatial
variability
using
kriging
showed
that
northeast
zone
is
most
solicited
terms
pumping
consumption
volume
exceeding
4,000
m3/month
reaching
2,500
kWh/month.
prediction
machine
learning
techniques
such
as
random
forest
support
vector
successfully
conducted.
tools
generated
by
methodology
applied
chosen
case
estimate
validate
results
obtained.
implemented
framework
better
Increasing
numbers
of
emerging
contaminants
(ECs)
detected
in
water
environments
require
a
detailed
understanding
these
chemicals’
fate,
distribution,
transport,
and
risk
aquatic
ecosystems.
Modeling
is
useful
approach
for
determining
ECs’
characteristics
their
behaviors
environments.
This
article
proposes
systematic
taxonomy
EC
models
addresses
gaps
the
comprehensive
analysis
applications.
The
reviewed
include
conventional
quality
models,
multimedia
fugacity
machine
learning
(ML)
models.
Conventional
have
higher
prediction
accuracy
spatial
resolution;
nevertheless,
they
are
limited
functionality
can
only
be
used
to
predict
contaminant
concentrations
Fugacity
excellent
at
depicting
how
travel
between
different
environmental
media,
but
cannot
directly
analyze
variations
parts
same
media
because
model
assumes
that
constant
within
compartment.
Compared
other
ML
applied
more
scenarios,
such
as
identification
assessments,
rather
than
being
confined
concentrations.
In
recent
years,
with
rapid
development
artificial
intelligence,
surpassed
becoming
one
newest
hotspots
study
ECs.
primary
challenge
faced
by
outcomes
difficult
interpret
understand,
this
influences
practical
value
an
some
extent.
The Science of The Total Environment,
Год журнала:
2024,
Номер
944, С. 173999 - 173999
Опубликована: Июнь 13, 2024
Membrane
technologies
have
become
proficient
alternatives
for
advanced
wastewater
treatment,
ensuring
high
contaminant
removal
and
sustainable
resource
recovery.
Despite
significant
progress,
ongoing
research
efforts
aim
to
further
optimize
treatment
performance.
Among
the
challenges
faced,
membrane
fouling
persists
as
a
relevant
obstacle
in
technologies,
necessitating
development
of
more
effective
mitigation
strategies.
Mathematical
models,
widely
employed
predicting
performance,
generally
exhibit
low
accuracy
suffer
from
uncertainties
due
complex
variable
nature
wastewater.
To
overcome
these
limitations,
numerous
studies
proposed
artificial
intelligence
(AI)
modeling
accurately
predict
technologies'
performance
mechanisms.
This
approach
aims
provide
simulations
predictions,
thereby
enhancing
process
control,
optimization,
intensification.
literature
review
explores
recent
advancements
membrane-based
processes
through
AI
models.
The
analysis
highlights
enormous
potential
this
field
efficiency
technologies.
role
defining
optimal
operating
conditions,
developing
strategies
mitigation,
novel
improving
fabrication
techniques
is
discussed.
These
enhanced
optimization
control
driven
by
ensure
improved
effluent
quality,
optimized
consumption,
minimized
costs.
contribution
cutting-edge
paradigm
shift
toward
examined.
Finally,
outlines
future
perspectives,
emphasizing
that
require
attention
current
limitations
hindering
integration
plants.
Journal of Marine Science and Engineering,
Год журнала:
2025,
Номер
13(4), С. 636 - 636
Опубликована: Март 22, 2025
Oil
spills
and
marine
litter
pose
significant
threats
to
ecosystems,
necessitating
innovative
real-time
monitoring
solutions.
This
research
presents
a
novel
AI-driven
multisensor
system
that
integrates
RGB,
thermal
infrared,
hyperspectral
radiometers
detect
classify
pollutants
in
dynamic
offshore
environments.
The
features
dual-unit
design:
an
overview
unit
for
wide-area
detection
directional
equipped
with
autonomous
pan-tilt
mechanism
focused
high-resolution
analysis.
By
leveraging
multi-hyperspectral
data
fusion,
this
overcomes
challenges
such
as
variable
lighting,
water
surface
reflections,
environmental
interferences,
significantly
enhancing
pollutant
classification
accuracy.
YOLOv5
deep
learning
model
was
validated
using
extensive
synthetic
real-world
datasets,
achieving
F1-score
of
0.89
mAP
0.90.
These
results
demonstrate
the
robustness
scalability
proposed
system,
enabling
pollution
monitoring,
improving
conservation
strategies,
supporting
regulatory
enforcement
sustainability.
Advances in environmental engineering and green technologies book series,
Год журнала:
2025,
Номер
unknown, С. 353 - 370
Опубликована: Март 7, 2025
The
use
of
Artificial
Intelligence
(AI)
in
ecology
for
sustainability
has
given
a
new
face
to
jungle
and
environmental
health
monitoring,
management,
conservation.
AI
is
used
manage
resources
control
the
processes
connected
with
water,
energy
or
biodiversity
which
contributes
circular
economy.
applications
are
related
risks
through
their
predictive
abilities
climate
modeling,
pollution
management.
It
illustrates
potential
decision-making
ecological
conservation
immediately
cases
such
as
smart
air
water
quality
intelligent
farming
activities
species/preservation.
recognized
having
many
advantages
but
there
also
concerns
around
data
privacy,
ethical
dimensions
fact
current
algorithms
would
not
work
well
predict
impacts.
future
possibility
address
global
sustainable
development
goals
governance
becoming
evident.
