Deleted Journal,
Год журнала:
2025,
Номер
14, С. 85 - 98
Опубликована: Апрель 4, 2025
Lagoons
have
a
great
importance
for
society,
and
activities
such
as
fishing
or
tourism
are
essential
these
areas,
this
reason
it
is
important
to
monitoring
system
in
terms
of
water
quality.
The
central
axis
project
was
the
design
implementation
sensor
network
based
on
Internet
Things,
collecting
data
using
an
ESP32
Thingspeak
platform
visualization
storage.
Data
analyzed
MATLAB,
allowing
obtain
estimation
quality
index
Laguna
Jucutuma
indicating
average
rating
40,
well
Machine
Learning
techniques
models
with
error
margin
below
3%.
Heliyon,
Год журнала:
2024,
Номер
10(6), С. e27920 - e27920
Опубликована: Март 1, 2024
Water
holds
great
significance
as
a
vital
resource
in
our
everyday
lives,
highlighting
the
important
to
continuously
monitor
its
quality
ensure
usability.
The
advent
of
the.
Internet
Things
(IoT)
has
brought
about
revolutionary
shift
by
enabling
real-time
data
collection
from
diverse
sources,
thereby
facilitating
efficient
monitoring
water
(WQ).
By
employing
Machine
learning
(ML)
techniques,
this
gathered
can
be
analyzed
make
accurate
predictions
regarding
quality.
These
predictive
insights
play
crucial
role
decision-making
processes
aimed
at
safeguarding
quality,
such
identifying
areas
need
immediate
attention
and
implementing
preventive
measures
avert
contamination.
This
paper
aims
provide
comprehensive
review
current
state
art
monitoring,
with
specific
focus
on
employment
IoT
wireless
technologies
ML
techniques.
study
examines
utilization
range
technologies,
including
Low-Power
Wide
Area
Networks
(LpWAN),
Wi-Fi,
Zigbee,
Radio
Frequency
Identification
(RFID),
cellular
networks,
Bluetooth,
context
Furthermore,
it
explores
application
both
supervised
unsupervised
algorithms
for
analyzing
interpreting
collected
data.
In
addition
discussing
art,
survey
also
addresses
challenges
open
research
questions
involved
integrating
(WQM).
IntechOpen eBooks,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 5, 2025
Water,
sometimes
referred
to
as
the
‘matrix
of
life’,
highlights
fundamental
significance
life’s
ecosystem.
However,
water
pollution
creates
substantial
worldwide
concerns,
jeopardising
access
safe
drinking
and
impeding
progress
towards
Sustainable
Development
Goals
(SDGs).
Real-time
monitoring
(RTM)
systems,
which
use
modern
sensor
technology
data
analytics,
present
a
possible
answer
these
issues.
The
study
examines
challenges
presented
by
issues
such
scarcity,
insufficient
sanitary
infrastructure.
This
emphasised
function
RTM
in
management,
emphasising
its
benefits
for
improving
quality
monitoring,
supporting
effective
management
strategies
protecting
resources.
Furthermore,
it
investigates
Internet
Things
(IoT)
devices
remote
sensing
techniques
detection,
their
ability
give
real-time
data,
increase
capabilities
promote
informed
decision-making.
chapter
also
advanced
sensors
(chemical
sensors,
smart
satellite
sensors),
analytics
visualisation
approaches
enhanced
decision-making
resource
management.
Overall,
RTM,
when
combined
with
IoT
technologies,
provides
holistic
strategy
addressing
pollution,
mitigating
effects
promoting
sustainable
practices.
Water Research,
Год журнала:
2024,
Номер
258, С. 121777 - 121777
Опубликована: Май 16, 2024
The
determination
of
water
quality
heavily
depends
on
the
selection
parameters
recorded
from
samples
for
index
(WQI).
Data-driven
methods,
including
machine
learning
models
and
statistical
approaches,
are
frequently
used
to
refine
parameter
set
four
main
reasons:
reducing
cost
uncertainty,
addressing
eclipsing
problem,
enhancing
performance
predicting
WQI.
Despite
their
widespread
use,
there
is
a
noticeable
gap
in
comprehensive
reviews
that
systematically
examine
previous
studies
this
area.
Such
essential
assess
validity
these
objectives
demonstrate
effectiveness
data-driven
methods
achieving
goals.
This
paper
sets
out
with
two
primary
aims:
first,
provide
review
existing
literature
selecting
parameters.
Second,
it
seeks
delineate
evaluate
principal
motivations
identified
literature.
manuscript
categorizes
into
methodological
groups
refining
parameters:
one
focuses
preserving
information
within
dataset,
another
ensures
consistent
prediction
using
full
It
characterizes
each
group
evaluates
how
effectively
approach
meets
predefined
objectives.
study
presents
minimal
WQI
approach,
common
both
categories,
only
has
successfully
reduced
recording
costs.
Nonetheless,
notes
simply
number
does
not
guarantee
savings.
Furthermore,
classified
as
dataset
demonstrated
potential
decrease
whereas
have
been
able
mitigate
issue.
Additionally,
since
approaches
still
rely
initial
chosen
by
experts,
they
do
eliminate
need
expert
judgment.
further
points
formula
straightforward
expedient
tool
assessing
quality.
Consequently,
argues
employing
solely
reduce
enhance
standalone
solution.
Rather,
objective
should
be
integrated
more
research
critical
analysis
characterization
lay
groundwork
future
research.
will
enable
subsequent
proposed
can
achieve
Arabian Journal for Science and Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 18, 2024
Abstract
Identifying
and
measuring
potential
sources
of
pollution
is
essential
for
water
management
control.
Using
a
range
artificial
intelligence
models
to
analyze
quality
(WQ)
one
the
most
effective
techniques
estimating
index
(WQI).
In
this
context,
machine
learning–based
are
introduced
predict
WQ
factors
Southeastern
Black
Sea
Basin.
The
data
comprising
monthly
samples
different
were
collected
12
months
at
eight
locations
Türkiye
region
in
Sea.
traditional
evaluation
with
WQI
surface
was
calculated
as
average
(i.e.
good
WQ).
Single
multiplicative
neuron
(SMN)
model,
multilayer
perceptron
(MLP)
pi-sigma
neural
networks
(PS-ANNs)
used
WQI,
accuracy
proposed
algorithms
compared.
SMN
model
PS-ANNs
prediction
modeling
first
time
literature.
According
results
obtained
from
ANN
models,
it
found
provide
highly
reliable
approach
that
allows
capturing
nonlinear
structure
complex
series
thus
generate
more
accurate
predictions.
analyses
demonstrate
applicability
instead
using
other
computational
methods
both
particular
resources
general.
Tellus A Dynamic Meteorology and Oceanography,
Год журнала:
2024,
Номер
76(1), С. 177 - 192
Опубликована: Янв. 1, 2024
Tellus
A:
Dynamic
Meteorology
and
Oceanography
is
an
open
access
journal
focusing
on
all
aspects
of
atmospheric
dynamics
related
to
Earth
science
processes.
A,
along
with
its
sister
B:
Chemical
Physical
Meteorology,
are
international,
peer-reviewed
journals
the
International
Meteorological
Institute
in
Stockholm,
independent
not-for-profit
body
integrated
into
Department
at
Faculty
Sciences
Stockholm
University,
Sweden.
The
two
serve
international
community
researchers,
policymakers,
managers,
media
general
public.
Together
they
promote
exchange
knowledge
about
meteorology
from
across
a
range
scientific
sub-disciplines.
Topics
covered
A
include:dynamic
|
physical
oceanography
data
assimilation
techniques
numerical
weather
prediction
climate
modelling
observation.
Types
papers
accepted
include
original
research
papers,
review
articles,
brief
notes,
Letters
Editor,
special
issues
conference
proceedings
(from
time
time).
operates
single-blind
peer-review
policy.
All
published
articles
made
freely
permanently
available
online
through
gold
publication
CC
BY
license.
Read
Guidelines
for
Authors
more
information
how
submit
your
manuscript
review.
