Journal of Natural Resources and Agricultural Ecosystems,
Год журнала:
2023,
Номер
1(2), С. 63 - 76
Опубликована: Янв. 1, 2023
Highlights
Machine
Learning
(ML)
models
are
identified,
reviewed,
and
analyzed
for
HAB
predictions.
Data
preprocessing
is
vital
efficient
ML
model
development.
toxin
production
monitoring
limited.
Abstract.
Harmful
algal
blooms
(HABs)
detrimental
to
livestock,
humans,
pets,
the
environment,
global
economy,
which
calls
a
robust
approach
their
management.
While
process-based
can
inform
practitioners
about
enabling
conditions,
they
have
inherent
limitations
in
accurately
predicting
harmful
blooms.
To
address
these
limitations,
potentially
leverage
large
volumes
of
IoT
data
aid
near
real-time
evolved
as
tools
understanding
patterns
relationships
between
water
quality
parameters
expansion.
This
review
describes
currently
used
forecasting
HABs
freshwater
ecosystems
presents
structures
application
related
toxins.
The
revealed
that
regression
trees,
random
forest,
Artificial
Neural
Network
(ANN),
Support
Vector
Regression
(SVR),
Long
Short-Term
Memory
(LSTM),
Gated
Recurrent
Unit
(GRU)
most
frequently
monitoring.
shows
models'
prowess
identifying
significant
variables
influencing
growth,
drivers,
multistep
prediction.
Hybrid
also
improve
prediction
algal-related
through
improved
optimization
techniques
variable
selection
algorithms.
often
focus
on
biomass
prediction,
few
studies
apply
limitation
be
associated
with
lack
high-frequency
datasets
development,
exploring
this
domain
encouraged.
serves
guide
policymakers
researchers
implement
reveals
potential
decision
support
early
Keywords:
Cyanobacteria,
Freshwater,
blooms,
learning,
Water
quality.
Bioengineering,
Год журнала:
2022,
Номер
9(4), С. 153 - 153
Опубликована: Апрель 3, 2022
As
of
27
December
2021,
SARS-CoV-2
has
infected
over
278
million
persons
and
caused
5.3
deaths.
Since
the
outbreak
COVID-19,
different
methods,
from
medical
to
artificial
intelligence,
have
been
used
for
its
detection,
diagnosis,
surveillance.
Meanwhile,
fast
efficient
point-of-care
(POC)
testing
self-testing
kits
become
necessary
in
fight
against
COVID-19
assist
healthcare
personnel
governments
curb
spread
virus.
This
paper
presents
a
review
various
types
detection
diagnostic
technologies,
surveillance
approaches
that
or
proposed.
The
provided
this
article
should
be
beneficial
researchers
field
health
policymakers
at
large.
Water Science & Technology Water Supply,
Год журнала:
2023,
Номер
23(2), С. 895 - 922
Опубликована: Фев. 1, 2023
Abstract
Managing
water
resources
and
determining
the
quality
of
surface
groundwater
is
one
most
significant
issues
fundamental
to
human
societal
well-being.
The
process
maintaining
managing
well
involves
complications
due
human-induced
errors.
Therefore,
applications
that
facilitate
enhance
these
processes
have
gained
importance.
In
recent
years,
machine
learning
techniques
been
applied
successfully
in
preservation
management
planning
resources.
Water
researchers
effectively
used
integrate
them
into
public
systems.
this
study,
data
sources,
pre-processing,
methods
research
are
briefly
mentioned,
algorithms
categorized.
Then,
a
general
summary
literature
presented
on
determination
management.
Lastly,
study
was
detailed
using
investigations
two
publicly
shared
datasets.