Eutrophication
is
a
major
environmental
issue
with
many
negative
consequences,
such
as
hypoxia
and
harmful
cyanotoxins
production.
Monitoring
coastal
eutrophication
crucial,
especially
for
island
countries
like
the
Republic
of
Cyprus,
which
are
economically
dependent
on
touristic
sector.
Additionally,
open-sea
aquaculture
industry
in
Cyprus
has
been
exhibiting
an
increase
last
decades
monitoring
to
identify
possible
signs
mandatory
according
legislation.
Therefore,
this
modelling
study,
two
different
types
Artificial
Neural
Networks
(ANNs)
developed
based
situ-data
collected
from
stations
located
waters
Cyprus.
Theses
ANNs
aim
model
phenomenon
data-driven
procedures.
Firstly,
self-organizing
map
(SOM)
ANN
examines
several
water
quality
parameters
(specifically
temperature,
salinity,
nitrogen
species,
ortho-phosphates,
dissolved
oxygen
electrical
conductivity)
interactions
Chlorophyll-a
parameter.
The
SOM
enables
us
visualize
monitored
relationships
comprehend
complex
biological
mechanisms
related
A
second
feed-forward
also
predicting
levels.
Based
model,
scenarios
associated
eutrophication-related
can
be
extracted.
combination
these
models
considered
holistic
approximation
identification
scenarios,
since
it
not
only
prediction
parameter
levels,
but
“capturing”
hidden
algal
Journal of Scientific Reports-A,
Journal Year:
2024,
Volume and Issue:
058, P. 135 - 161
Published: Sept. 29, 2024
This
research
aims
to
evaluate
the
effectiveness
of
machine
learning
algorithms
in
determining
potability
water.
In
study,
a
total
3276
water
samples
were
analyzed
for
10
different
features
that
determine
Besides
that,
study's
consideration
is
impact
trimming,
IQR,
and
percentile
methods
on
performance
algorithms.
The
models
built
using
nine
classification
(Logistic
Regression,
Decision
Trees,
Random
Forest,
XGBoost,
Naive
Bayes,
K-Nearest
Neighbors,
Support
Vector
Machine,
AdaBoost,
Bagging
Classifier).
According
results,
filling
missing
data
with
population
mean
handling
outliers
Trimming
IQR
improved
models.
Forest
Tree
most
accurate
findings
this
are
high
importance
sustainable
resource
management
serve
as
crucial
input
decision-making
process
quality
study
also
offers
an
example
researchers
working
datasets
contain
values
outliers.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(22), P. 4196 - 4196
Published: Nov. 11, 2024
This
review
examines
the
integration
of
remote
sensing
technologies
and
machine
learning
models
for
efficient
monitoring
management
lake
water
quality.
It
critically
evaluates
performance
various
satellite
platforms,
including
Landsat,
Sentinel-2,
MODIS,
RapidEye,
Hyperion,
in
assessing
key
quality
parameters
chlorophyll-a
(Chl-a),
turbidity,
colored
dissolved
organic
matter
(CDOM).
highlights
specific
advantages
each
platform,
considering
factors
like
spatial
temporal
resolution,
spectral
coverage,
suitability
these
platforms
different
sizes
characteristics.
In
addition
to
this
paper
explores
application
a
wide
range
models,
from
traditional
linear
tree-based
methods
more
advanced
deep
techniques
convolutional
neural
networks
(CNNs),
recurrent
(RNNs),
generative
adversarial
(GANs).
These
are
analyzed
their
ability
handle
complexities
inherent
data,
high
dimensionality,
non-linear
relationships,
multispectral
hyperspectral
data.
also
discusses
effectiveness
predicting
parameters,
offering
insights
into
most
appropriate
model–satellite
combinations
scenarios.
Moreover,
identifies
challenges
associated
with
data
quality,
model
interpretability,
integrating
imagery
models.
emphasizes
need
advancements
fusion
techniques,
improved
generalizability,
developing
robust
frameworks
multi-source
concludes
by
targeted
recommendations
future
research,
highlighting
potential
interdisciplinary
collaborations
enhance
sustainable
management.
Eutrophication
is
a
major
environmental
issue
with
many
negative
consequences,
such
as
hypoxia
and
harmful
cyanotoxins
production.
Monitoring
coastal
eutrophication
crucial,
especially
for
island
countries
like
the
Republic
of
Cyprus,
which
are
economically
dependent
on
touristic
sector.
Additionally,
open-sea
aquaculture
industry
in
Cyprus
has
been
exhibiting
an
increase
last
decades
monitoring
to
identify
possible
signs
mandatory
according
legislation.
Therefore,
this
modelling
study,
two
different
types
Artificial
Neural
Networks
(ANNs)
developed
based
situ-data
collected
from
stations
located
waters
Cyprus.
Theses
ANNs
aim
model
phenomenon
data-driven
procedures.
Firstly,
self-organizing
map
(SOM)
ANN
examines
several
water
quality
parameters
(specifically
temperature,
salinity,
nitrogen
species,
ortho-phosphates,
dissolved
oxygen
electrical
conductivity)
interactions
Chlorophyll-a
parameter.
The
SOM
enables
us
visualize
monitored
relationships
comprehend
complex
biological
mechanisms
related
A
second
feed-forward
also
predicting
levels.
Based
model,
scenarios
associated
eutrophication-related
can
be
extracted.
combination
these
models
considered
holistic
approximation
identification
scenarios,
since
it
not
only
prediction
parameter
levels,
but
“capturing”
hidden
algal
