AIMS Mathematics,
Год журнала:
2024,
Номер
9(10), С. 26916 - 26950
Опубликована: Янв. 1, 2024
<p>Accurate
prediction
of
sewage
flow
is
crucial
for
optimizing
treatment
processes,
cutting
down
energy
consumption,
and
reducing
pollution
incidents.
Current
models,
including
traditional
statistical
models
machine
learning
have
limited
performance
when
handling
nonlinear
high-noise
data.
Although
deep
excel
in
time
series
prediction,
they
still
face
challenges
such
as
computational
complexity,
overfitting,
poor
practical
applications.
Accordingly,
this
study
proposed
a
combined
model
based
on
an
improved
sparrow
search
algorithm
(SSA),
convolutional
neural
network
(CNN),
transformer,
bidirectional
long
short-term
memory
(BiLSTM)
prediction.
Specifically,
the
CNN
part
was
responsible
extracting
local
features
from
series,
Transformer
captured
global
dependencies
using
attention
mechanism,
BiLSTM
performed
temporal
processing
features.
The
SSA
optimized
model's
hyperparameters
to
improve
accuracy
generalization
capability.
validated
dataset
actual
plant.
Experimental
results
showed
that
introduced
mechanism
significantly
enhanced
ability
handle
data,
effectively
hyperparameter
selection,
improving
training
efficiency.
After
introducing
SSA,
CNN,
modules,
$
{R^{\text{2}}}
increased
by
0.18744,
RMSE
(root
mean
square
error)
decreased
114.93,
MAE
(mean
absolute
86.67.
difference
between
predicted
peak/trough
monitored
within
3.6%
appearance
2.5
minutes
away
time.
By
employing
multi-model
fusion
approach,
achieved
efficient
accurate
highlighting
potential
application
prospects
field
treatment.</p>
Water Research X,
Год журнала:
2024,
Номер
26, С. 100291 - 100291
Опубликована: Дек. 3, 2024
Sudden
shocking
load
events
featuring
significant
increases
in
inflow
quantities
or
concentrations
of
wastewater
treatment
plants
(WWTPs),
are
a
major
threat
to
the
attainment
treated
effluents
discharge
quality
standards.
To
aid
real-time
decision-making
for
stable
WWTP
operations,
this
study
developed
probabilistic
deep
learning
model
that
comprises
encoder-decoder
long
short-term
memory
(LSTM)
networks
with
added
capacity
producing
probability
predictions,
enhance
robustness
effluent
prediction
under
such
events.
The
LSTM
(P-ED-LSTM)
was
tested
an
actual
WWTP,
where
bihourly
total
nitrogen
performed
and
compared
classical
models,
including
LSTM,
gated
recurrent
unit
(GRU)
Transformer.
It
found
events,
P-ED-LSTM
could
achieve
49.7%
improvement
accuracy
predictions
concentration
GRU,
A
higher
quantile
data
from
output,
indicated
value
more
approximate
real
quality.
also
exhibited
predictive
power
next
multiple
time
steps
scenarios.
captured
approximately
90%
over-limit
discharges
up
6
hours
ahead,
significantly
outperforming
other
models.
Therefore,
model,
its
robust
adaptability
fluctuations,
has
potential
broader
applications
across
WWTPs
different
processes,
as
well
providing
strategies
system
regulation
emergency
conditions.
Ecological Indicators,
Год журнала:
2024,
Номер
163, С. 112100 - 112100
Опубликована: Май 8, 2024
The
alarming
increase
in
the
frequency
of
blooms
Microcystis
freshwater
lakes
and
reservoirs
occurs
worldwide,
with
major
implications
for
their
ecosystem
functioning
water
quality.
dominance
is
tightly
related
to
colonial
formation
by
Microcystis.
However,
studies
development
morphospecies
are
rare.
This
research
applied
FlowCAM-based
imaging
flow
cytometry
analyze
mesocosms
mimicking
eutrophic
shallow
effect
temperature
changes.
A
significant
positive
association
was
found
between
M.
ichtyoblabe,
aeruginosa,
smithii
colonies,
particularly
high-temperature
tanks,
suggesting
that
these
belong
one
ecocluster,
which
supports
hypothesis
central
transition
pathways
small
clusters
cells
represented
an
important
stage
sequence
bloom
were
associated
forms.
correlation
analysis
showed
higher
pH
positively
correlated
abundance
M.wesenbergii
independently
sheaths'
abundances
increased
following
a
maximum
abundance,
reaching
numbers
(thousands),
majority
sheaths
contained
at
least
some
cells.
We
hypothesize
may
be
crucial
spp.
dispersal
represent
obligatory
colonies
development.
protect
against
environmental
stress
factors,
improve
cell
survival
low
nutrient
levels,
participate
spreading.
Our
findings
can
applicable
early
CyanoHAB
detection
management
dispersal.
