Current Opinion in Environmental Sustainability,
Год журнала:
2023,
Номер
62, С. 101290 - 101290
Опубликована: Апрель 23, 2023
Water
governance
is
facing
rapid
transformations
as
cyber-physical
systems
(CPS)
are
deployed
across
water-related
sectors
and
river
basins.
These
CPS
—
often
considered
artificial
intelligence-enabled,
automated
or
'smart'
technological
promoted
for
improving
monitoring,
management
of
hydrological
systems.
We
review
recent
applications
CPS,
highlighting
their
diverse
functions
the
water
cycle,
including
in
rural,
urban
coastal
settings.
then
focus
on
how
smart
technologies
connect
to
people,
policy
ecosystems.
Key
our
argument
that
integrating
social
ecosystem
dimensions
into
research
design
will
be
vital
sustainable
governance,
per
a
cybernetic
approach.
This
includes
consideration
data
requirements,
end-user
experience,
sociopolitical
environmental
impacts,
well
acceptability,
CPS.
Eco-Environment & Health,
Год журнала:
2022,
Номер
1(2), С. 107 - 116
Опубликована: Июнь 1, 2022
With
the
rapid
increase
in
volume
of
data
on
aquatic
environment,
machine
learning
has
become
an
important
tool
for
analysis,
classification,
and
prediction.
Unlike
traditional
models
used
water-related
research,
data-driven
based
can
efficiently
solve
more
complex
nonlinear
problems.
In
water
environment
conclusions
derived
from
have
been
applied
to
construction,
monitoring,
simulation,
evaluation,
optimization
various
treatment
management
systems.
Additionally,
provide
solutions
pollution
control,
quality
improvement,
watershed
ecosystem
security
management.
this
review,
we
describe
cases
which
algorithms
evaluate
different
environments,
such
as
surface
water,
groundwater,
drinking
sewage,
seawater.
Furthermore,
propose
possible
future
applications
approaches
environments.
Water Research,
Год журнала:
2022,
Номер
223, С. 118973 - 118973
Опубликована: Авг. 11, 2022
Deep
learning
techniques
and
algorithms
are
emerging
as
a
disruptive
technology
with
the
potential
to
transform
global
economies,
environments
societies.
They
have
been
applied
planning
management
problems
of
urban
water
systems
in
general,
however,
there
is
lack
systematic
review
current
state
deep
applications
an
examination
directions
where
can
contribute
solving
challenges.
Here
we
provide
such
review,
covering
demand
forecasting,
leakage
contamination
detection,
sewer
defect
assessment,
wastewater
system
prediction,
asset
monitoring
flooding.
We
find
that
application
still
at
early
stage
most
studies
used
benchmark
networks,
synthetic
data,
laboratory
or
pilot
test
performance
methods
no
practical
adoption
reported.
Leakage
detection
perhaps
forefront
receiving
implementation
into
day-to-day
operation
systems,
compared
other
reviewed.
Five
research
challenges,
i.e.,
data
privacy,
algorithmic
development,
explainability
trustworthiness,
multi-agent
digital
twins,
identified
key
areas
advance
management.
Future
expected
drive
towards
high
intelligence
autonomy.
hope
this
will
inspire
development
harness
power
help
achieve
sustainable
digitalise
sector
across
world.
Water,
Год журнала:
2024,
Номер
16(2), С. 314 - 314
Опубликована: Янв. 17, 2024
Integration
of
the
Internet
Things
(IoT)
into
fields
wastewater
treatment
and
water
quality
prediction
has
potential
to
revolutionize
traditional
approaches
address
urgent
challenges,
considering
global
demand
for
clean
sustainable
systems.
This
comprehensive
article
explores
transformative
applications
smart
IoT
technologies,
including
artificial
intelligence
(AI)
machine
learning
(ML)
models,
in
these
areas.
A
successful
example
is
implementation
an
IoT-based
automated
monitoring
system
that
utilizes
cloud
computing
ML
methods
effectively
above-mentioned
issues.
The
been
employed
optimize,
simulate,
automate
various
aspects,
such
as
managing
natural
systems,
water-treatment
processes,
wastewater-treatment
applications,
water-related
agricultural
practices
like
hydroponics
aquaponics.
review
presents
a
collection
significant
water-based
which
have
combined
with
IoT,
neural
networks,
or
undergone
critical
peer-reviewed
assessment.
These
encompass
chlorination,
adsorption,
membrane
filtration,
indices,
modeling
parameters,
river
levels,
automating/monitoring
effluent
aquaculture
Additionally,
this
provides
overview
discusses
future
along
examples
how
their
algorithms
utilized
evaluate
treated
diverse
aquatic
environments.
Environmental Science & Technology,
Год журнала:
2021,
Номер
55(16), С. 10895 - 10907
Опубликована: Авг. 2, 2021
The
advent
of
new
data
acquisition
and
handling
techniques
has
opened
the
door
to
alternative
more
comprehensive
approaches
environmental
monitoring
that
will
improve
our
capacity
understand
manage
systems.
Researchers
have
recently
begun
using
machine
learning
(ML)
analyze
complex
systems
their
associated
data.
Herein,
we
provide
an
overview
analytics
frameworks
suitable
for
various
Environmental
Science
Engineering
(ESE)
research
applications.
We
present
current
applications
ML
algorithms
within
ESE
domain
three
representative
case
studies:
(1)
Metagenomic
analysis
characterizing
tracking
antimicrobial
resistance
in
environment;
(2)
Nontarget
pollutant
profiling;
(3)
Detection
anomalies
continuous
generated
by
engineered
water
conclude
proposing
a
path
advance
incorporation
application.
Journal of Water Resources Planning and Management,
Год журнала:
2022,
Номер
148(7)
Опубликована: Апрель 21, 2022
A
rapidly
changing
digital
landscape
is
shifting
government-owned
infrastructure
utility
organizations
toward
transformation.
This
literature
review
aims
to
consider
how
the
characteristics
of
organizations,
in
particular
water
might
influence
their
transformation,
understand
issues
involved
that
and
identify
factors
could
support
digitization
pathways
for
these
organizations.
The
research
found
technologies,
social
behaviors,
expectations
around
transformation
will
continue
push
Changing
regulatory
requirements
a
greater
focus
on
increasing
efficiency
improving
customer
relationships
also
drive
this
corporatewide
governance,
culture,
skills,
knowledge,
coupled
with
"single
point
truth"
data
management,
enable
operations,
community
relations,
smart
systems
achieve
economic
performance
efficiencies,
improved
satisfaction,
better
compliance.
Dynamic
network
modeling
may
complex
nature,
internal
external
relationships,
interdependencies,
current
maturity.
require
careful,
long-term
strategic
organizational
planning
commitment.
